{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "10783cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "import talib as ta\n",
    "from matplotlib import pyplot as plt\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e43ab38",
   "metadata": {},
   "outputs": [],
   "source": [
    "pro = ts.pro_api('0ba8feef618e5db7b1ebb65538fe51e4aef69fb3cbf709d44128f313')\n",
    "\n",
    "f_time = lambda x:  datetime.datetime.strptime(x, \"%Y%m%d\")\n",
    "f_week = lambda x: int(x.strftime(\"%w\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b42bc00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.46</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.28</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.0776</td>\n",
       "      <td>576604.00</td>\n",
       "      <td>541571.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.49</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.30</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.2151</td>\n",
       "      <td>624593.27</td>\n",
       "      <td>586343.755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.42</td>\n",
       "      <td>9.27</td>\n",
       "      <td>9.30</td>\n",
       "      <td>9.34</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.4283</td>\n",
       "      <td>421140.60</td>\n",
       "      <td>393215.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2018-12-25</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.21</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.42</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.8493</td>\n",
       "      <td>586615.45</td>\n",
       "      <td>545235.607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>9.40</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.42</td>\n",
       "      <td>9.45</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.3175</td>\n",
       "      <td>509117.67</td>\n",
       "      <td>477186.904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code trade_date  open  high   low  close  pre_close  change  pct_chg  \\\n",
       "0  000001.SZ 2018-12-28  9.31  9.46  9.31   9.38       9.28    0.10   1.0776   \n",
       "1  000001.SZ 2018-12-27  9.45  9.49  9.28   9.28       9.30   -0.02  -0.2151   \n",
       "2  000001.SZ 2018-12-26  9.35  9.42  9.27   9.30       9.34   -0.04  -0.4283   \n",
       "3  000001.SZ 2018-12-25  9.29  9.43  9.21   9.34       9.42   -0.08  -0.8493   \n",
       "4  000001.SZ 2018-12-24  9.40  9.45  9.31   9.42       9.45   -0.03  -0.3175   \n",
       "\n",
       "         vol      amount  \n",
       "0  576604.00  541571.004  \n",
       "1  624593.27  586343.755  \n",
       "2  421140.60  393215.140  \n",
       "3  586615.45  545235.607  \n",
       "4  509117.67  477186.904  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pro.daily(ts_code='000001.SZ', start_date='20180101', end_date='20181231')\n",
    "df.trade_date = df.trade_date.apply(f_time)\n",
    "df.set_index('trade_date')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b671664c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1 = df\n",
    "import talib as ta\n",
    "df1['ma1'] = ta.SMA(df1.close.values, timeperiod=3)\n",
    "df1['ma2'] = ta.SMA(df1.close.values, timeperiod=7)\n",
    "df1['trend'] = 0\n",
    "# df1.loc[con]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb0a538e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>ma1</th>\n",
       "      <th>ma2</th>\n",
       "      <th>trend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181228</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.46</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.28</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.0776</td>\n",
       "      <td>576604.00</td>\n",
       "      <td>541571.004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181227</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.49</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.30</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.2151</td>\n",
       "      <td>624593.27</td>\n",
       "      <td>586343.755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181226</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.42</td>\n",
       "      <td>9.27</td>\n",
       "      <td>9.30</td>\n",
       "      <td>9.34</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.4283</td>\n",
       "      <td>421140.60</td>\n",
       "      <td>393215.140</td>\n",
       "      <td>9.320000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181225</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.21</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.42</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.8493</td>\n",
       "      <td>586615.45</td>\n",
       "      <td>545235.607</td>\n",
       "      <td>9.306667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181224</td>\n",
       "      <td>9.40</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.42</td>\n",
       "      <td>9.45</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.3175</td>\n",
       "      <td>509117.67</td>\n",
       "      <td>477186.904</td>\n",
       "      <td>9.353333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code trade_date  open  high   low  close  pre_close  change  pct_chg  \\\n",
       "0  000001.SZ   20181228  9.31  9.46  9.31   9.38       9.28    0.10   1.0776   \n",
       "1  000001.SZ   20181227  9.45  9.49  9.28   9.28       9.30   -0.02  -0.2151   \n",
       "2  000001.SZ   20181226  9.35  9.42  9.27   9.30       9.34   -0.04  -0.4283   \n",
       "3  000001.SZ   20181225  9.29  9.43  9.21   9.34       9.42   -0.08  -0.8493   \n",
       "4  000001.SZ   20181224  9.40  9.45  9.31   9.42       9.45   -0.03  -0.3175   \n",
       "\n",
       "         vol      amount       ma1  ma2  trend  \n",
       "0  576604.00  541571.004       NaN  NaN      0  \n",
       "1  624593.27  586343.755       NaN  NaN      0  \n",
       "2  421140.60  393215.140  9.320000  NaN      0  \n",
       "3  586615.45  545235.607  9.306667  NaN      0  \n",
       "4  509117.67  477186.904  9.353333  NaN      0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "912e3790",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'con_long' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-c2cd8b5ebad1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcon_long\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'trend'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcong_short\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'trend'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'con_long' is not defined"
     ]
    }
   ],
   "source": [
    "df1.loc[con_long, 'trend'] = 1\n",
    "df1.loc[cong_short, 'trend'] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86959ca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['pos'] = 100 * df1['trend'].shift()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c1cc2d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nJReFExMe07Ee7qyzTLizbbKy4Cc/gRdfhNWrfTL0oOL3cOZwOCgvLx80gcW2bcrLy3E4HP4eioiIiIicJgZqzVlKTIo5sGkTTJgAMTFMT50OdJ3S+NprkJICOTlmrzP32GbMgKNH3XMZr73WFNReecUnQw8qfm8IMmLECAoKCigr803nmd5obGwc0LDkcDgYMaJrpxsREREREW/KG3zbrbGktoQhjiFEhEaYAxs3wtKlgKmmjUoYxegho93XO52mcnbhhabAlhyT7Fk5AxPwRo0iMhLmzYPcXJ8MPaj4PZyFh4eTlZXl1zHk5uaSk5Pj1zGIiIiIiHjjbHNS2VgJ+LZy5p7SWFxs/nGFLOCNL71BoiPR/femTVBebhp+wHGVs+nTTe/8jRth2TIAFi6EH/8YKipg6FCfvEJQ8Pu0RhERERER6Z4rmKXFplHfUk99S32/f0dJXUnXZiAzZrjPj08a77EH2muvgWXBxz5m/k6OTjYNQQCiomDixI7nYMKZbcPatf0+9KCicCYiIiIiEsBcUxrHDRtn/m7v3NifSmo7Vc42bTLJKzu72+tfew1mzYKk9uaNydHJnlMuZ8wwlbP2vhKzZ5vMtmZNvw89qCiciYiIiIgEMFczkPHDxgO+mdroUTnbuBHGjYO4OK/XHjsG773XMaURzLRGj6reWWdBSQkUFQFo3VkvKZyJiIiIiAQwV6VsfJIJZ/3dFKSxtZHqpmrPaY2d1psdb+VKaGvzDGfJMclAp+A4a5b5XL/efc3ChbB1q1l3Jt4pnImIiIiIBDBX5cw1rbG/K2eldaVA+x5nZWWmBX6n9WbHe+01GDKkI3+BmdboMbazzoKICHj3Xfc1rnVnb73Vr8MPKgpnIiIiIiIBzNfhzL0BdUxqRxOPHipnubmwaBGEder7nhRtFp+5m4JERsLMmR7hzLXuTFMbu6dwJiIiIiISwMobyrGwyErMwsLq/3DWvgF1amyqWW8GZmdpLwoK4MABOO88z+OuaY0eUy7nzYMNG6CxETCFtPnzFc56onAmIiIiIhLAKhoqGBI1hPDQcIZFD/N95WzMGEhM9HqtqxX+8eHMVTnzGNv8+dDc3KWl/tatZo806UrhTEREREQkgFU0VDAsahhgQlB/NwRxVc5SYlJM5ayH9WZvvw3x8TBtmufxREcioVZox7RGgLPPNp/HrTsDrTvrjsKZiIiIiEgAK28oZ2jUUMCEM19UzuIi4oiqaYCDB3tcb7Z2rZmtGBrqeTzECukaHFNTTRXunXfch2bNguhoePPNfn2FoKFwJiIiIiISwCoaKnwbzuraN6DetcscmDLF63Xl5bB9e9cpjS5exzZ/vqmctW9GHREBCxbA6tX9NfrgonAmIiIiIhLAKhoqGBbdPq0xykfhLCbVdPoAU+3yYt0683nuud6fkxyT3HXK5bx5UFoK+/e7Dy1danJgQcGpjjz4KJyJiIiIiASw8vpyhjpM5Sw5Jpmj9Uex2ytR/aGktr1y5gpQmZler3v7bdMhv/P+Zp15rZzNm2c+O01tXLrUfKp61pXCmYiIiIhIgGpta6WqqcpjWmNrWyvVTdX99h3uytn+/ZCeDg6H1+vWroU5c0xA8yY5OtmzIQjApEmmg0inpiBTpkBKCqxa1V9vEDwUzkREREREAlRlYyVAx7RG12bP/dSxscXZQkVDRce0xtGjvV5XW2s64ne33gxMOKtoqKC1rbXjYGio6drYKZyFhMCSJSac9WMBMCgonImIiIiIBKjyerMhWOfKGdBv685K60oBOqY1ZmV5ve5//wOns/v1ZgCTkidhY7P8H8upaKjoODFvHuTlQVWV+9DSpXDkCOzY0S+vETQUzkREREREApQr5LjCWXJ0MgDHjhbAG2/ArbeaMtRJ9qZ37XGWFj7UdOjopnK2dm1HEaw7n538WR75xCO8vu91zvrDWawvXE9rWyvOuXNMiey999zXutadaWqjpzB/D0BERERERLwrbzCVs86bUKfWwNL5X4b6RggLA8uCJ56AxYv7/PyimiIARlbZJkB1E87WrIGcHIiL6/5ZlmXxvdnfY3bGbD7z7GeY86c5ACQ0QCVwYM2/yLrgAgBGjYKxY004u/baPg87aCmciYiIiIgEqOMrZ0nRSUw8CuH1jfDrX8NVV8EVV3T0ue+jdw6/Q1hIGBOr27t8eJnW+I9/mMfff3/vnjk7Yzabv7WZJzY/QUNrA3XNdeT/7n5aP9zscd3SpfDXv0JLC4SHn9Twg46mNYqIiIiIBKjjw1lsRCyj6kLNyY99DGJjzUKwgwdPauOwVQdWMXfEXKLyi82B4ypn+flw9dUwdy7ccEPvnzs0aig3zLuBO867gzsX3Mm2FEjYe9jjmqVLTaOR9ev7POygpXAmIiIiIhKgyuvLCbFCSHAkAGbq4NiGaHMyI8N8urp09LF6dqzhGBuLNrIka4np1BgZCWlp7vNOJ3zlK9DaCn/7m5lBeTKiwqLYMTyEoYfLTJms3aJFZkbmypUn99xgpHAmIiIiIhKgKhoqGOIYQojV8Z/to+sjqY8K61gANn26qaCtXdunZ+cezMXGZunopaZTY2am6XPf7qGHIDcXHnkExow5+XewLIv9GdGEtThh71738SFDIDv7pGdkBiWFMxERERGRAFXRWOGe0ugyosaiNLHTIq2wMNNGsY/hbNX+VcSExzA7Y7YJZ52mNBYVwZ13wqc+ZZa0naqCM4aYf8nL8zg+ezZs2ABtbaf+HcFA4UxEREREJECV15e7N6B2GV7VRlGc5XnhOeeY4HPsWK+fvfrAas474zwiQiO6bED9m9+YGYg//7mZeniqjo5KwhliwbZtHsdnzTLbn+3bd+rfEQwUzkREREREAlRFQ9fKWVJlE/mxrZ4XnnuuaYX/7ru9em5BdQG7y3eb9WbHjkFlpbtTY10d/P73sHx5t531+yw6bij5qY4ulbNZs8znBx/0z/ec7hTOREREREQCVEVDhXuPMwDa2kioqGd/dDOtbZ0C2pw5ZnpjLxdwrd6/GqBjvRm4k9hTT0FFBVx/fb+8AgAJjgR2Dw/vUjmbNAmiohTOXBTOREREREQCVHlDuWflrKyMUGcbhXEdbfYBiI6GGTN6ve5s9YHVJEUnMTV1qpnSCDB6NG1t8MtfmrVg8+b133skRiayLcU2QbCuzn08LAzOOkvt9F0UzkREREREAlCLs4XqpmrPcFZYaD7i4Wj9Uc8bzj3XlKAaG3t8rm3brD6wmsVZi00XSFflLCuLV181DRWvv75/1pq5JDgS2JTUYqZe7tjhcW7WLNi82aPL/qClcCYiIiIiEoAqGysBPKc1FhUBUBjnJZydcw40N59wjuCuo7soqiliadZSc2D/fhg2DOLj+cUvYNQo06WxPyU6EvlgaHto9NKxsbERtm/v3+88HSmciYiIiIgEoPKGcgCvlbOiOLj77bvZcmQLAHXNdTwStgmA1X/5SY/PfevQWwAszlpsDrR3aty40exrds01J7/hdHcSIhPYPwTsqCivHRtB686gF+HMsqzHLcsqtSwrz8u5GyzLsi3LSvLN8EREREREBifXmrIu4SwkhGsvvY8NRRvI+UMOn/j7Jxj98Giu2XAXeSmQ9L8Pe3zunvI9RIdHM3pIeyvG/fshK4uf/QwSEuAb3+j/d0l0JNIWAs3jx3QJZ2PGmA2pFc56Vzl7Evj48QctyxoJfAw43M9jEhEREREZ9MrrTeXMY5+zwkJITeWHC27lwLUH+PGCH7OhaANTU6ay7qvr2DlnDJN2lZvNw45j26ZI9u6qZIblXwFY4HTCoUPsTZzFc8/Bd74D8fH9/y4JjgQAasdldZnWaFkwc6bCGfQinNm2/TZQ4eXUL4GbALu/ByUiIiIiMth1WznLyABMNeonC39C2Q/LWPWVVcwfNZ8D8yYS3ga88YbHs1asgMRE0y3//QdvJf/3v+V3v2t/XksLD+y8iIgIuPZa37xLoiMRgGNnZsCRI3DUc73crFmmoFZf75vvP12c1Jozy7IuBQpt2+65ZioiIiIiIifFFc6GOIZ0HCwqcoczbxpmZlPhgLZXXnYfs224+25z22OPQcy3l5A5exvXXgvvvlhGEWn85b3xXHUVpKb65l0SIk3lrGR0+xd42Yza6YQtW3zz/aeLPi/1sywrGrgNM6WxN9d/E/gmQGpqKrm5uX39Sp+rra0NyHFJ8NJvTgaafnPiD/rdyUALtt9c3iETYDb+byNhIeY/2+cfOkRpVhZ7u3nPqtI6/jsWPvXii7y3ejWEhrJ/fwy7ds3iBz/Yw/DM3dQVvclnv/oErxfcy/JbxvAJ7qPVaXHOOe+Rm9tzG/6TVdxQDEBu81HmA3tfeIHCTudbWiKAeaxYsZfm5kJvjwhI/f2bO5k+LGOALOBDy2x+MALYZFnWbNu2jxx/sW3bjwGPAcycOdNeuHDhyY/WR3JzcwnEcUnw0m9OBpp+c+IP+t3JQAu239zK1SsJOxzG0sXtLe8bGqC6mozZs8no5j2rdlXxzLhfcvm2ahbGxMDcuaxeDSEhcMst4yh01sG7cNF58/nBa1HMzbH4C1fy+c/CF78412fvUtFQAeshJnsMJCYy1ulk7HHvkJYGlZVjWbhwrM/G0d/6+zfX52mNtm1vs207xbbtTNu2M4EC4CxvwUxERERERE5OfUs9UWFRHQfa9zjraVpjelw6r4+BttAQeOUVbBv++U9YtAhSUuBA5QEAsoZkMXUqPDH0RoY7jnHbbb58E4iPNF1GqpqqYdw42LOnyzUzZ8KmTb4dR6DrTSv9p4H/AeMtyyqwLOtrvh+WiIiIiMjg1tDaQHR4dMeB9j3OegpnaXFpHIuGkmmj4dVX+fBDk4M++1lz/mDlQQAyEzOhtJTPlv2Gorv+zNSpvnkHl7CQMGIjYs3G2mPHeg1nU6bA3r1mH+3BqjfdGr9g23aabdvhtm2PsG37z8edz7Rt+2h394uIiIiISN81tDYQFe6lcpae3u09qTGpWFjkzcqELVv455+qCQ2Fyy4z5w8cO0B8ZLxpMvL++wBYc+f46A08JToSqWqqMpWzw4fNNM1OJk+G1lavuW3QOKlujSIiIiIi4ltdpjX2onIWHhpOUnQS704fig388582S5ZAUpI5f7DqIJmJmViWZcJZaCjMmOG7l+gkITLBVM7GjTMHPvrI4/zkyeZz+/YBGU5AUjgTEREREQlADS1epjVGR0NCQo/3pcelszGxns3pF/NRWYJ7SiOYyllWYpb54/33YepU88wB4FE5gy4lsgkTTOMShTMREREREQkoXaY1ujagNh3Tu5UWl0Zx3RH+OeoGwmhh+WxTcbNtm4OVB004a2uD9ethzsBMaQRIcCR0rDmDLuHM4YAxYxTOREREREQkwHid1tjDlEaXtNg0imuKebViHgvJZehzjwFwtP4odS11phnI7t1QXT2g4SzRkUhVYxXExZm++d00BVE4ExERERGRgNJlWmNRUY/NQFzSYtMoLmklb08ES8YVwB//CC0tHm30Xc1ABrRy5lpzBt12bJw82XRsbPTNXtgBT+FMRERERCQAeUxrtG0TznpTOYtLo+3gfAAWfO1MKC6GV17xbKP//vsQH28Weg0Q15oz27a73ets8mQz43L37gEbVkBROBMRERERCUAe0xrLy6GpqVfhLD0uHQ4uwBHlZOY182DkSPjd7zhwrL1yltheOZs1y3TgGCAJkQm0trVS31JvwllZGVRWelwz2Ds2KpyJiIiIiAQgj2mNvWij75IWmwYHFzJpRgXhjlD4xjdg5UrqdnzIsKhhxDlDYevWAZ3SCKZyBni209+71+OaceNMd3+FMxERERERCRgelbNebEDtEtmcAaXTyJx+yBz4+tchNJS5T6/lguoUeO45cDoHPJwlOMwWAD2104+MNMvRBms4C/P3AERERERExJNt255rzioqzOewYSe8d+/m4ebSSXnATNMZ8bLLuPDZZ7kwF+AKU57yZ+Vs9AwzpbKbdWcffjigQwsYqpyJiIiIiASYJmcTQMe0xqoq83mCDagB3l0bAeF1hKRvch9r+91vuezyMJ74yTJ48UV47z1ITe33cffEFc6qGqtMieyMM7oNZx99BLcbHmoAACAASURBVA0NAzq8gKBwJiIiIiISYOpb6gE6pjW6wlli4gnvzc2FmDEfUtpU4D5WEtHCv8a20nDh+fDJT8LMmf095BNKiDTB0t1Ov4eOjbYNu3YN5OgCg8KZiIiIiEiAaWgxZSP3tMaqKoiIAIejx/vKy02vj+FTdlNcW+w+7t7jLDHLNwPuBXflrKk9aLrCmW17XOfq2JiXN5CjCwwKZyIiIiIiAaah1YQzj2mNvZjSuHat+Twzp4CimiL3cVcb/czEzH4dZ1+4GoJ4VM5qa+HIEY/rxo6F8PDB2RRE4UxEREREJMB4ndbYi3CWmwtRUTA1p5EjtUfMhs/guQG1n0SFRREeEm7WnEG37fQjIswphTMREREREfE717RGd+WssrJX4eytt2DePBgxJIVmZzMVDabL4zv57zAyfmTHNEk/sCyLBEeCZ+UMul13pnAmIiIiIiJ+55rW6LHm7AThzLZh926YPh3S4tIAKK4tZk/5Hv67779clXOVT8fcG4mOxI41ZyNHmjKZl3CWnQ0HDpg1dIOJwpmIiIiISIA5mWmN5eWm/fyoUZAWa8JZUU0RD7//MBGhEXx75rd9OubeSIjsVDkLDTULzLy0ZZw713yuX39y37Pr6C5KaktOcpT+o3AmIiIiIhJgukxr7EU4O3zYfI4aBelx6QDsLNvJE1ue4ItTv0hq7MDua+aNR+UMYOJE2Lmzy3UzZ4Jlme3YTsayZ5bxvf9+7yRH6T8KZyIiIiIiAeZkpjUeOmQ+zzijY1rjA+8+QH1LPdfOudZnY+0LjzVnAJMmwf790NjocV1cHEyZAu+/3/fvsG2bguoCMuIyTnG0A0/hTEREREQkwHhMa3Q6oaamT5Wz6PBo4iPjKawpZGHmQrKHZ/t6yL2SGJnoGc4mToS2Nq/rzubONeGsra1v31HdVE1dS53CmYiIiIiInDqPaY01NeZgL8JZVBQMG2b+dq07u27OdT4bZ18lOBI6WumDqZyB16mNc+eaJpXHddo/ocKaQgBGxI842WH6jcKZiIiIiEiA8ZjWWNUeZnoRzkaNMmu1ALKGZHHm0DO5eNzFvhxqnyQ6EqlrqaPF2WIOjBsHISGwY0eXa+fMMZ99XXdWWG3CWUa8KmciIiIiInKK6lvqsbCIDI3sdTg7dMisN3P58yf/zJtfeZPQkFAfjrRvEiLNO1Q3VZsDDgeMHu21cjZxIsTH9z2cFVQXAGhao4iIiIiInLqGlgaiwqOwLKsjnCUm9niPq3Lmkh6XzsiEkT4cZd8lOsw7dFl35qVyFhICs2f3vSmIa1qjKmciIiIiInLK6lvqPfc4gx4rZ42NUFLiGc4CUYLDvINHO/1Jk0xDkNbWLtfPmQNbt0JdXe+/o7C6kGFRw3CEOU51uANO4UxEREREJMA0tDZ4ttGHHsNZgZnJF/DhrNvKWUsLfPRRl+vnzjXNKjdu7P13FNYUnpZVM1A4ExEREREJOA2tDZ4bUEOP4azzHmeBzLXmrLcdG11NQfoytfF03eMMFM5ERERERAJOX6c1dt7jLJB5rZxNmGA+vaw7S042/UL60hSksKbwtGyjDwpnIiIiIiIBx9UQBDDhLCLCdDbsxuHDpoX+iADPJF7XnMXFwciRXitnYKY29jacNTubKa0rVeVMRERERET6h8e0xsrKXu1xlpZmMlwgS4hMINQKpbim2PNENx0bAc4+G4qKvC5J66Kopgg4PTs1gsKZiIiIiEjA6TKtsRd7nAX6lEaA0JBQZmXM4u3Db3uemDQJdu2CtrYu91x4ofl86aUTP9+1AbWmNYqIiIiISL/oMq2xF5Wz0yGcASzJWsL6wvVdm4LU13csnutk9GiYMqWX4cy1x5mmNYqIiIiISH/o0q2xh3Bm26dfOGuz23jr0FsdBydONJ/drDu79FJYuxYqKnp+dkG12VNA0xpFRERERKRf9GVaY2kpNDUFfht9l7NHnk1UWBSr96/uOOgKZ92sO/vkJ81+Z6++2vOzC6sLiQqLYohjSD+NdmApnImIiIiIBJiGlt5Xzk6XNvoujjAH54w6h9UHOoWzYcMgJaXbytnMmabhyYmmNro2oLYsqx9HPHAUzkREREREAkxDa4Nn5SwxsdtrT7dwBmZq4/ay7R5dGw9lxFL47mterw8JgUsugddeM1XC7hTWFJ62681A4UxEREREJKC0OFtobWs1DUGcTqipCarKGcDS0UsBePPAmwBsL93Oitj9pOwuhNpar/dceqk5tWZN988tqC44bdebgcKZiIiIiEhAaWhtADDTGmtqzMEewtmhQxAbC0NOo2VW2cOzGeIY4p7aeOPKG3kzE8LbgHfe8XrP4sUQEwMvvuj9mbZtU1RTxIi407ONPiiciYiIiIgElIYWE86iwqLMlEY4YeVs1Cg4nZZZhYaEsjhrMav2r+K1fa/x2r7XODI1i5YQaFr1htd7HA644AKz7sy2u54/Wn+UZmezqZw9+yy8/76P36L/KZyJiIiIiASQ+pZ6ADOtsQ/h7HSzJGsJ+dX5fP2lr3Pm0DO5fumdrM+AtjWru73n0kuhqAg2bux6zt1GPyYNvvtd+PWvfTV0n1E4ExEREREJIB7TGoM5nI1eApgmHj9f+nMyEzNZkwmRW7Z1TOc8zic+YSqE//lP13OuDajHH6iGsjK4+GJfDd1nFM5ERERERAJIX6Y11tebHHK67HHW2dihYxkzZAwLMxeybMIy0uPSyc2EEGcbrFvn9Z7kZJg9u5twVm3C2ah120x7x49/3Iej9w2FMxERERGRANKXaY35+ebzdKycWZbF2q+u5cXPv4hlWaTHpfPuSHCGhUJubrf3XXghrF9vQmlnBdUFhFghxK18C+bNg6FDffsCPqBwJiIiIiISQPoyrfF0bKPfWVpcGvGR8QDERcYRGhvLofGpPfbLv+gi0xDkteO2RCusKWR6axLW5i2n5ZRGUDgTEREREQko7spZWBRUVpqDJwhnp+O0Rm/S49LZNCHRdPyorvZ6TU4OpKbCq696Hi+sKeTTB9o37lY4ExERERGRU+Vec+aa1hgRYfrIe3HokFlelZ4+kCP0nfS4dNaODoG2Nli71us1ISGmMcjrr0Nra8fxguoClu5sgsxMmDRpYAbczxTOREREREQCSJdpjSfo1JieDuHhAzU630qPS2dlSq0JpD2sO7voIlNUfO8987dt25SXFzA976g5eTpt+taJwpmIiIiISADxmNbYi3B2uq438yYtNo2DTSXYc+f2uO7s/PMhLKxjauNr+14jZ3c1kU2tp+2URlA4ExEREREJKF2mNSYmdnvt4cPBs94MTOWsobWBpnPPhk2boKLC47xt22wt2Up8vM0555iW+rZt89O3fsoXDsZiR0fDwoXs2AHHjvnpJU6BwpmIiIiISABxTWs8UeWsrc200g+myll6nFk8Vzx3imnJeFz1bM3BNUz//XQe+t9DXHghbN0KK955m/cL3mf5/gis889nX4GDxYvh8sv98QanRuFMRERERCSA1LfUExEaQWhIaI/hrKQEmpuDM5ztH5sEcXGwcqXH+T3lewC4edXNJOeYBWff+XocC0snE1dcQfG5n+VjHzONQh56aGDH3h8UzkREREREAkhDS4OpmkGP4ex03+PMG1c4K2wshYULu4Szw1WHCQsJY/yw8fxw8yV84+61VH80kZ1Pvckq63wu+PNnKS010x0nTvTDC5wihTMRERERkQDS0NpgOjVCr8JZMK05S4tNA6Copsh0/di/3/zTLr86n4y4DP71uX/R1NrEH53nkfT9ZUQ2t3K+/Qa79oXx73/D7Nn+eoNTo3AmIiIiIhJA6lvqTTMQpxNqaroNZ4cOmc9gqpzFRMSQEJnQEc7Ao3p2uOowoxJGMT5pPE8tfwoLi4fnnc2Glul8dc4Onn8eli710+D7gcKZiIiIiEgAcVfOamrMgR4qZ/HxPXbaPy2lx6WbcDZ+PIwY4TWcASybsIzSH5byhX0OkjnK48/Gcckl/hp1/1A4ExEREREJIO41Z1VV5kAP4SyYqmYu7nBmWaZ69uab4HTibHNSWF3IyPiR7muTopPghRdg1iwYObKHp54eFM5ERERERAKIe1rjCcLZoUPBtd7MJS0uzYQzMHMUjx2DjRspqSuhpa3FXTkDzF4CH3wAl13mn8H2M4UzEREREZEA4p7WOFgrZ7HpFNcWY9t2xwKyVas4XGU6oHiEs3//23wqnImIiIiISH9zT2usrDQHvISz2lqoqAjScBaXTrOzmYqGCkhJgenTYeVK8qvyARiZ0Gn64gsvwOTJMG6cn0bbvxTOREREREQCSG+mNeabnBK04QzomNp4/vnwzjsc3Z8HdKqcHTgAb78dNFUzUDgTEREREQkoDa0NRIf1PK3R1UY/GNecdQlnX/86hIZy3t1PERceS0Jkgtlm4IorIDbWnA8SCmciIiIiIgHEXTmrrjYHvIQz1wbUg6JyNn48/PznTP7gINdvi8OyLHjoIVi7Fh59NKj+R1A4ExEREREJIO41ZzU1EBYGkZFdrjl8GEJDIS3NDwP0sbQ481LucAbw3e/y3sQ4bvlXKTz3HNxxB3z60/ClL/lplL6hcCYiIiIiEiDa7DaanE0dm1DHxZn9vo5z+LDZnzkszA+D9DFHmIOhUUM9w1lICN+8LBxneBh85jOQlAS//73X/21OZwpnIiIiIiIBoqGlAcBMa3SFMy8OHQqq2XxdpMelU1TbEc4aWhrYFl7BazcuM+vMHn8chg3z4wh9Q+FMRERERCRANLS2h7OwnsNZsO5x5pIWm+ZROSuoLgCgbtmFZg+Bj3/cX0PzKYUzEREREZEA4aqcRYdHm4Yg8fFdrnE6oaAguMNZelw6hdWF7r89NqAOD/fXsHxO4UxEREREJEDUt9QDPU9rPHIEWluDs42+y/TU6RTWFPJRxUcA5Febjd3ce5wFKYUzEREREZEA0ZtpjQcOmM9gDmefmvQpAJ7b8RzQUTnLiMvw25gGwgnDmWVZj1uWVWpZVl6nY3dblrXVsqwtlmW9YVlWum+HKSIiIiIS/DymNZ4gnGVlDeTIBtaohFHMzpjNczs7wtnw2OFEhnXdViCY9KZy9iRw/Iq7B2zbnmbbdjbwCvCj/h6YiIiIiMhg05tpjfv3mw7ywVw5A/j0xE+zoWgDBysPcrjqcNBPaYRehDPbtt8GKo47Vt3pzxjA7udxiYiIiIgMOu5pjaGOHitn6engcAz06AaWa2rj8zueJ786n5HxI/08It876TVnlmXda1lWPnA5qpyJiIiIiJwy17TGGGeoacvYTeVs9OiBHtnAGz1kNGelncWzO54dNJWzk95T3Lbt24HbLcu6Ffge8GNv11mW9U3gmwCpqank5uae7Ff6TG1tbUCOS4KXfnMy0PSbE3/Q704GWjD85jYe2QjAznfXMwnYU1xM0XHvtGvXXHJyKsnN3TXwAxxgZznO4k8H/gRAc1lzwP3/29+/uZMOZ538HfgP3YQz27YfAx4DmDlzpr1w4cJ++Mr+lZubSyCOS4KXfnMy0PSbE3/Q704GWjD85nZt2AW74bxJ2QCMmzGDcZ3eqakJjh6FefOGs3DhcD+NcuCkl6fzp0dNOFt01iIWTlro3wEdp79/cyc1rdGyrLGd/rwUCP7YLiIiIiLiY65pjY6GFnPguE2oDx0C2x4c0xoBxg0bx7TUaUDw73EGvaicWZb1NLAQSLIsqwBTIbvQsqzxQBtwCLjal4MUERERERkMXN0a3eHsuDVn+/ebz2Buo3+8z03+HNtLt5M1JPhf+oThzLbtL3g5/GcfjEVEREREZFBraG0g1AolrM5U0I4PZ649zgZL5Qzgxnk3cuHYC0mKTvL3UHzupLs1ioiIiIhI/6pvqScqPAqrttYc8FI5i4yE4cG/3MwtIjSC7OHZ/h7GgFA4ExEREREJEPUt9USFtW9ADV4rZ1lZEKL/ig9K+r9VRERERCRAlNaVkhKT0m04279/cK03G2wUzkREREREAkRRTRHpcekd4Sw21uP8gQODa73ZYKNwJiIiIiISIAprCsmIzzDhLDoaQkPd544dg8pKVc6CmcKZiIiIiEgAcLY5Ka4pJj02Haqr1alxEFI4ExEREREJAGX1ZThtZ0fl7LgNqAfjHmeDjcKZiIiIiEgAKKwuBOhYc9ZN5UzhLHgpnImIiIiIBICimiIAMuIyvIaz/fth6FBISPDH6GQgKJyJiIiIiASAwhpTOXNPa/RSOdN6s+CmcCYiIiIiEgAKqwsJsUI69jnTHmeDjsKZiIiIiEgAKKopYnjscMJCwrqEM6cTDh1S5SzYKZyJiIiIiASAwppC0wwEuoSzoiJoblblLNgpnImIiIiIBICimiLTDKS1FerrPcKZ9jgbHBTOREREREQCgLtyVltrDnQKZ4cPm89Ro/wwMBkwCmciIiIiIn7W2NpIRUNFRxt98NiEuqDAfI4Y4YfByYBROBMRERER8TP3HmfxncJZp8pZQQEMGQIxMf4YnQwUhTMRERERET8rrDZ7nKXHpXcbzjIy/DEyGUgKZyIiIiIifuaunMV5r5wVFmpK42CgcCYiIiIi4meFNSeunCmcBT+FMxERERERPyuqKSIqLIpER2KXcNbcDCUlmtY4GCiciYiIiIj4mauNvmVZUF1tDraHs+JisG1VzgYDhTMRERERET8rrC40nRqhS+Ws0Mx4VDgbBBTORERERET8rKimyDQDARPOwsLA4QC0x9lgonAmIiIiItIL9759L8ueWcb7Be/363Nt23ZPawRMOIuLA8sCOsKZ1pwFP4UzEREREZFeeDrvaV7c/SJz/zyXi1dczLaSbf3y3MrGShpbGz0rZ8d1aoyOhsTEfvk6CWAKZyIiIiIivZBfnc+V2Vdy3+L7eDf/XT72t4/RZred8nM92uhDl3Dm2uOsvZAmQUzhTERERETkBKqbqqluqmZi0kRuPfdWfv3xX3Ok9ggfHvnwlJ/t3oA6vvvKmaY0Dg4KZyIiIiIiJ5BflQ/AyPiRACwZvQSAVftXnfKzC6tN5aynaY1qBjI4KJyJiIiIiJxAfnV7OEsw4Sw9Lp2JSRNZfWA1/Oc/cNll4HSe1LNd0xrT4tLMgU7hrK0NiooUzgYLhTMRERERkRM4vnIGsCRrCTt3vo19xRXwr3/Bnj0n9eyimiKGRQ3DEWZa51Nd7Q5npaXQ2qpwNlgonImIiIiInEBBdQEWVkfTDszUxvtfasA6etQc2LLlpJ7t0UYfPCpnaqM/uCiciYiIiIicQH51PmlxaYSHhruPnb+1ji/kQe6XzoGIiJMOZ+X15SRFJ5k/bNuEs/h4QBtQDzYKZyIiIiIiJ5Bfne8xpZFjx4i59kb2jIjmR+e2wuTJJx3OaptriYtsbwDS2GjWrh1XOVM4GxwUzkRERERETiC/Kt/dDASA++6DsjJev/1zvHvkA5qnTobNm03lq4/qWuqIjYg1f9TUmM/2cFZYCOHhkJx8qm8gpwOFMxERERGRHti2TX51PiPiOpWvcnNhwQImX/AlnLaTPSOjoKwMiov7/Pza5lpiw72Hs4ICSE+HEP1X+6Cg/5tFRERERHpwrPEY9S31HZWzxkb48EOYPZt5I+fhCHOwJuGYOXcSUxtrm2u7rZxpj7PBReFMRERERKQHXdrof/ghtLTA7Nk4whzMHzmfFaHbzbk+hrM2u4265p6nNapT4+ChcCYiIiIi0oOCatOVw105W7/efM6eDcCCMxbwXs1O2rKy+hzOGloasLGJiYgxB6qrzWdcHLatytlgo3AmIiIiItKD/OrjKmfr15uFYO0lLVdoa5g8rs/hrLa5FsBr5ezYMWhoUDgbTBTORERERER6kF+VT1hIGMNjh5sD77/vrpoBpMSkAFAxfhTs29cRsHqhp3CmNvqDj8KZiIiIiEgP8qvzSY9LJzQkFCoqYO9er+GseEyqaaW/bVuvn91tOIuPp7DQ/KvWnA0eCmciIiIiIj3w2IB6wwbz6SWc7T8j3hzow9TGupY6wEs4i41V5WwQUjgTEREREelBflU+I+LbE5KrGcjMme7zydFmh+gDsS0wdGifwpnXyll0NISGsncvRESY5W0yOCiciYiIiIh0w7ZtCqoLPJuBTJgACQnua6LCo4iLiKO0vgyys08tnFVVQbypwG3dCpMmQVhY/7yLBD6FMxERERGRbhytP0qTs8l0ZLRtE846TWl0SYlJobS+1ISzbdugtbVXz+8Szo4cgdRUwISzadP65z3k9KBwJiIiIiLSDY82+vn5UFICc+Z0uS4lJoXSuvZw1tgIu3f36vldwllxMaSlUVZm/lXhbHBROBMRERER6UZ+VXs4SxjZZfPpztzhzJWmetmx0RXOYsLbN6EuLob0dPftCmeDi8KZiIiIiEg3PCpn69ebDh1eEpM7nE2YAKGhfQpnFhZR4VHgdJrKXFoaW7ea8wpng4vCmYiIiIhIN/Kr8okIjSA5JtmEs+xsE9COkxKTQlldGW0R4TB+fJ/CWUxEDCFWCJSVmYDWHs5SUtzLz2SQUDgTEREREelGfnU+GXEZhNjApk0wa5bX61JiUnDaTvblV7J11MW0fdi7cFbXXOe53gwgLY1t21Q1G4wUzkREREREulFQXWDWm+3da/YgmzGjyzU7dsATN34aHixk/BlDmf7azzjj8NvcfF0TeXk9P7+2pbZLOHOmppOXp3A2GCmciYiIiIh043DVYbPebONGc8BLOLv2Wtj7YTKMWcl3bv+Ix6/ZwnQ+5KFHI5g6FX7zm+6fX9vcKZwVFQGwr2kkjY0KZ4ORwpmIiIiIiBd1zXUcrjrM+GHjYcMGcDjMrtCdbNoEq1bBN649CsuvZOEXNvHVHyTyCpdQ9H9PccklcM018MYb3r/DI5y1V862HkkBFM4GI4UzEREREREvdh7diY3NlJQppnKWnQ1hYR7XPPAAxMXBd64OBTAdG884A+LiSDn0AStWwNSp8JnPwM6dXb+jSzgbOpStO8MJDYWJE339hhJoFM5ERERERLzIKzULxqYkTTIlsuOmNB44AP/8J1x9NWSlDcXCMuHMskwi27qV2Fh46SVTdLv4Yigv9/yOLuGsvVPj+PHmHhlcFM5ERERERLzIK83DEeZgdFkr1NbCzJke53/5S7Ol2bXXQlhIGMOih5lwBiacbdsGts2oUfDii5CfD3fc4fkdtc21HRtQFxVBejpbt2pK42ClcCYiIiIi4kVeaR6TkicRunmLOdCpcnb0KPzpT3D55ZCRYY6lxKRQWt8pnFVWQmEhAHPnwpVXwhNPwJEjHd9xfOWsathoDh5UOBusFM5ERERERLzIK81jcvJks94sKspjEdijj0JDA9x4Y8f1KTEpnpUz8NiM+oc/hOZmePjhjnvc+5zZNhw5Ql7odEDhbLBSOBMREREROc6xhmMU1hSaZiAbNng0Aykrg1/8ApYvh8mTO+45UTgbOxY+/WnTWr+qClqcLTQ5m0w4Ky+Hlha2No0HFM4GK4UzEREREZHjbC/bDrQ3A9m82WNK4z33QH093Hef5z0p0Z3C2ZAhZr7j1q0e19x8M1RXwx/+AHUtdQAmnBUV0UIY/9gxhaFDYcQI372bBC6FMxERERGR47g6NWZXR3s0A9m/H373O/ja12DCBM97UmJSqGyspNnZbA5Mm+ZROQOT8c4/3zQTOVpdC5hwZhcV83X+xFs7UnjwQdPwUQYfhTMRERERkeNsL91OXEQcabtMQw9X5eyOO8zsxh//uOs9KTFm8+iyujJzYOpUs7lZS4vHdbfcYpqC/OS2GDiWSWxELHf8ZjhPcQV3XVfBV7/qs9eSABd24ktERERERAaXvLI8pqRMwdq0yTQDmTCBjRvh6afh9tshPb3rPa5wVlpXSkZ8hglnLS2wZ4/H4rRFi8zas7//eQhwgJteqSH/ozi+yR+4454vD9AbSiBS5UxEREREpBPbttlWss00A9m4EXJyICyM22+HYcPgppu839c5nAFem4KAmbL47LOw4u334PwbiU9o4/JxH/Cb+NuwYqJ99VpyGlA4ExEREZHTmrPNySt7XqG4obhfnldaV0p5QzlnRY2G9eth7lw2bIDXXzft8OPjvd/XJZyNG2eS2M6dXq+PSz0K8x/iiZf28LepPyMsPaVfxi+nL4UzERERETkttdltPLv9Wab9fhqXPH0Jt+bdSlNrk8c1q/av4rGNj/Xpua5mIAs2HIWmJvj85/m//4PERPj2t7u/z73mrL59zVlUFGRldRvO6po7dWssLoa0tD6NU4KPwpmIiIiInHZqmmqY86c5fPa5zwJwx7l3cKj+EA+8fCusXQt//zvFd9/M5qs+wQf3f582u63Xz3aFszH/+R+MG8eO6Jm88AJ8//vdV80A4iPjiQiN6Kicgdm4uptwVtvc0a1R4UxADUFERERE5DR0y6pb2Fi0kScufYIvZ1xI6PU3cN3LkQyr+iXwSwDSgB8CTguKP3yHjOxze/XsvNI8spuGErH2XbjrLn72c4voaLjmmp7vsyzLcyNqMOFs5UpwOiE01ON6dzgLj4GiIu9dRmRQUeVMRERERE4ruQdz+e2G33Ld3Ou4snkSoTNnwT/+QcPMs7n7oli++91Mvv3AQlJuCeU3z99Cawg4f35/r5+fV5bH1fsSATi44Ar+/nf41rcgKenE9yZHJ3cNZ83NcOBAl2td4SymvsVMn1TlbNBT5UxERERETht1zXV87aWvMWbIGO7fPRJuPMdUnNatY199PdOGV/GjfyyDuoM8svwRzh/+Re4cs5bbn1sHvzgCw4d3++z8qnzWHFzDtiNbuWS9A/vsedz711GEhMANN/RufF4rZ2CmNp55pse1tc21RIRGEFFabg4onA16JwxnlmU9DlwMlNq2PaX92APAJUAz8BHwVdu2K305UBERERGRO9fcyf5j+9mWdg8Rmq+BUwAAIABJREFU37oeLroInnoKhg6F3FwunXApN8+/GYBvTP8uZ59tsXnPOn5BK1Oml3L2ZbBwofknNdU880jtES742wVsLdkKwHnHEmg4lMgnop/m9T/Bd78LGRm9G19KTAq7ju7qONA5nF1yice1tc21HevNQOFMelU5exJ4FHiq07GVwK22bbdalvUz4Fbg5v4fnoiIiIgMZrZt8999/2XlRytZc3ANH5Z8yM1jrmTK9b82+4+98AJERHjcc/9SM4Xx5pth82YY/ZnHmPtONWUlZ/H3v6fx+99bgGmkCFBaFUNdw5ukZbQxeXw4mYd3MZlsIvIj+PWv4Tvf6f14XZUz27axLMu0eBw+3GtTkNqW9nBWVGQOKJwNeicMZ7Ztv21ZVuZxx97o9Od7wKf7d1giIiIiIvDklie56qWrcIQ5mDdyHv+3+D5+eP9aqK6Gv/2tSzBzWbMGHngAvvlNCL90K5vTnuSdh+tovfFeNn38NtasMcHNCm3lX/teICshlUkRH2fvHpt1e6dzafoH/PKDc/vcoyMlJuX/2bvv8KiqrYHDv0nvhSSkQwoQCJ3QFBAQBGxYERAU5SrotaB+6rXgtbdrxw6ioiAiiIACoiC99yCEEtJD+kz6pJ/vj52ZEBIgZRJA1vs8PLmcOefsfXLjhDVr7bUxVhgpKi9SgRectWNjYVkhzrbONZkzaQhy2bNEQ5CpwGoL3EcIIYQQQohaPtvzGd3adiP3P7msu3sdzxz1xnrVanj7bYiMrPcavR7uugs6doT334dIn0i2tSmiZNTV2Hz0Hv0dovnPf+DHH+HqJ76h9Lp7+HaOE7/9BsfufYtinFg0t6hJsZJpr7O0gtM2xDYFZ5pW69yisqKaskZnZ3B1bfyA4h+lWQ1BdDrd80AFsOAc50wDpgH4+vqyYcOG5gzZIgoLCy/KeYl/LvmZE61NfubEhSA/d6K5Ygtj2XNqD490eITtW7Zjl53NgEcfJb9PHw527w5n/HyZfuZeeSWS9HRvPv10P7t3F1CWWwbAD9dHMWlfNNYDB3LkhRfIGTiQ1/e8TkeXjlTGVbJj6w/0e+kl9EOGcNjBoc79G0Ir0rDCiid+foL/66S6iATa2NAxP59tP/9M2WktH1OzUrHWWZN58CAuHh7skv9eLjmWfp9rcnCm0+nuQTUKGaFpZ3wMcBpN02YDswH69u2rDRs2rKlDtpgNGzZwMc5L/HPJz5xobfIzJy4E+bkTzbV45WIcbBx4+daX8XT0hDffhJISPBctYtgZnQ9B/cxlZg5j/Xp47TWYPj0KgMiiSB4/+Dj5/QOwP3AAxo6lx8yZHHvmfpJsEvn21nkM7zEMrr0W7O3xWbiQYQ3tAHKGYQwjxi6Gd7a9w2MjH2NE2Ai1x9msWVzp6ak6kVSzPmZNkFsQbYszoWNH+e/lEmTp97kmlTXqdLoxwNPAWE3Tii02GyGEEEIIIYDi8mIWHFrA7ZG3q8BM01RXxiFD6rSkN9Hrbfn3v6FvX9UMxMTHyQcvRy+OZB1RbRc3bYKbbybijS+JnmPDxJNOsGgRrFmjoromBmYmLw97mU5enbjv1/vUXmand2w8TWFZIW7WThAdDb16NWtM8c9w3uBMp9MtBLYDETqdLkWn0/0L1b3RFfhTp9Md0Ol0X7TwPIUQQgghxGVk8eHF5JXmcX+f+9WB3bvh6FGYMqXe8zUNPvywE4WFMG8e2JxWH6bT6ejatqsKzgCcnTn2+WvcdQv4W7lhe9s4uPNOiIpSffObydHWka/Hfk1ibiLPrn1WdWF0c6s3OAvPqgCjEfr0afa44tJ33uBM07SJmqb5a5pmq2lakKZpczVN66BpWrCmab2q/zzQGpMVQgghhBCXh9n7ZhPhFcGQdkPUgXnzwMEBbq+/SfjChbB5sw+vvlp/n5BI70iOZB3BtBrno90fs7iPPZWHD8G338Lw4fD112BtbZH5D2o3iEf6P8Inuz9hX/r+ejs2FpYVEpFQqP4SFWWRccWlzRLdGoUQQgghhLCYw5mH2Za8jfv63Kf2CistVdHXLbeAu3ud87dvhwcegMjIPJ54ov57RvpEYigxkFGUgd6oZ97BeUzqPom27gEqG7duHfToYdHnmHnVTAD+iv+rTnCmaRqFZYWExhnAyQkiIiw6trg0SXAmhBBCCCEuKkuOLMFKZ8XdPe9WB1auBIMB7r67zrnbtsGoUWqf55deOnzWxFekj0qnHck6wlf7vqK4vJgZA2e01CMA4OPsQ4BrAIcyD6ngLD0dcnMBMFYY0dAIis1U680slLETlzYJzoQQQgghhEUVlRXx75X/5lTBqSZdvy99HxFeEeY9w5g3T63bGjmy1nlbt8Lo0eql9evBx6fsrPc0BWcH0w/y8a6PuTr0anr4WjZTVp8evj2Izoiu0xSkqKwIXRX4nTgl682EmQRnQgghhBDCojYlbuLzPZ/z8oaXm3T9gfQD9PKr7l6YlQWrVsGkSbW6fGzZAmPGQECA2o7sfA0W/Vz88HDw4IMdH5CSn8LjAx9v0twaq0fbHhzJOkJ5p+oOk9XBWWFZIR31YGssk/VmwkyCMyGEEEIIYVGmrojfHPiGpLykRl2rN+pJykuqCc6WLIGKiloljacHZuvXq6/no9PpiPSJJDk/mQ5tOnBdx+saNa+m6uHbg7LKMo67lYO3N6xeDajgLMqUWJTMmagmwZkQQgghRDPkl+Yz78A8cxdAoYIzN3s3AN7a8lajrj2YfhCgJjhbtgw6doRu3QDYvFkFZkFBKmPWkMDMJNJblTbOGDADK13r/DPYVDoZnX0YJk+G5cshO5vCskL6pEGlvV397SXFZUmCMyGEEEKIZpi9dzb3LL+Ho9lHL/RULhqHsw4T5R/F1N5Tmbt/Lin5KeoFTYNXX1VR1VkcSD8AVAdneXkqNXbzzWRm6Xj+eRWYBQerw/7+jZvXqPBRdPHuwpSe9e+V1hIivCOwtbJV686mToXycvjhB5U5S4PiLuG1N2UTlzUJzoQQQgghmmFHyg4AYvWxrTZmeWX5RZup0zSNI1lHiPSJ5JnBz1ClVfH2lrfVi3PmwH//i3bffVBZWe/1BzIOEOAaoJqBrF5NfrkDj8Q9Rvv28OabcO21TQvMAMZ1HceRh47gau/ajCdsHDtrO7r4dCE6Mxq6d1fry+bOpbAknz5pUNqzW6vNRVz8JDgTQgghhGgGU3B20nCyVcYrKC2g7btt+eXoL60yXmOlFqRSUFZApE8kIR4h3NPzHubsm8PrX9+L8ZEHSXYD3cmTqlyxHvvT9ptLGrVflnGX3U98vsyfO++EI0fUEjQ/v9Z8oubr4duDQxmH1F+mToXoaLxWrse9FKp697qwkxMXFQnOhBBCCCGaKCU/hdSCVABO6lsnODuhP0FuSa65/O9iY2oGYmpd/+yQZ7Gu1Bjx0jwqbKyYMCOQFB97eOcdVeZ4mpKKEmKyY+jl2wtKS3l3eQdWlI3h3Xd1zJ0LnTu3+uNYRPe23UnOT8ZgNMDEiWBvT+8PFgJg3bf/BZ6duJhIcCaEEEII0USmrJmDjQNxuXGtMma8IR6AtIK0VhmvsUzBWVefrgCEeYaRXvEYA5M1XOd+zxWDJ/DOgErYuVNtVHbGtRVVFfTy68Wmjw/ybOlL3D4ojRktu1d0izM1BTmUeQg8PeHWW3FN11NqDQ49pY2+qCHBmRBCCCFEE+1I2YG9tT3XhF3TapmzhNwEANIKL97gzNvJGx9nH3VAr8f1rfdhwgSYMIF+Af34qkcFFZ7u8O67ta41ZQMDdVGMfzGCcF0cc5d6otO19lNYlrljY0a0OnDvvQAcaguOzu4XalriIiTBmRBCCCFEE+1I2UFUQBSdvTsTnxtPZVX9TS4sKT63OnN2EQdnppJGADZtUvuUPfwwAP0C+1FsBwdvHQQrVsCxY+ZTD6QfwNnWma/eDcFQbM+SEV/g1tahtR/B4vxd/PFy9KoJzkaMIL2dF5s72LZaS39xaZCfBiGEEEKIJiirLGNv2l4GBg4k3DOcssoy8/qzlmTOnF2EZY3mTo3epwVnGzaAoyP06wdAqEcoXo5ezB/iDnZ2MHOmuXPjgfQDdLYdxfffwf3MofuUf8bmzDqdjh6+PWqCMysrXvnoFv53g+eFnZi46EhwJoQQQgjRBNEZ0ZRUlDAwaCDhbcKB1mkKYsqcZRRltEqmrjEyijIwlBhqZ842boQrrlCBGCpQ6RfYj3XFf8MLL6j2i5MnU1VawoH0A9iuuh8qK3nS/hO47roL9CSW18O3B39n/k2VVgVAfpURZ4fWa+kvLg0SnAkhhBBCNMH25O0AKjjzVMFZnKFlm4JomkZCbgJOtk5UaVVkFWe16HiNdWanRgwGOHgQhg6tdV6/gH4czjpM0VOPwdtvw48/UnLDGK7f6MDBDUOZ7LCE9is/gzZtWvsRWkwP3x4UlReZG7oUlhXiYudygWclLjYSnAkhhBBCNMGO1B0EuAYQ5BZEsHswNlY2Lb7XWVZxFsXlxfQPVO3XL7bSxsOZh4HTgrMtW1S7/HqCsyqtiv3p++Hpp2H2bBzXbSJszQxKcOA/a0bAiBGtPf0WZWoKcjDjICDBmaifBGdCCCGEEE2wI2UHA4MGotPpsLGyIcQjRAVnZ+zdZUmm9WZXBl0JXHxNQY5kHcHDwQM/l+pdojduBHt7GDCg1nn9AtX6s92pu9WB++9n9swpvGfzEDfdXEnnq9q25rRbRVefrrjaufLEmifYlbpLgjNRLwnOhBBCCCEaKbMokzhDHFcEXWE+FuYZhtfW/RAaCgdaZoNoU0ncFcFq3Istc3Yk+whdfbqiM/W+37hRBWYOtTsu+rn4EeQWxO5TKjgzlht5Zn8nSis8mDnTtrWn3SocbR1Ze/daAAZ/PZi/M/+W4EzUIcGZEEIIIUQj7UzZCaj1Zib9ynx4dXYsJCbC7NktMq6pGYhp3Isxc2YuaczPh337YNiwes/tF9DPHJx9uOMjcrfdRs8BBqL+wXsy9w/sz77p+xjdYTRF5UUSnIk6JDgTQgghhGikXam7sNZZ08e/utW70cgj/9uAdaVG+ZBBsGgRlJVZfNyE3AS8nbzxdvLG08HzosqcZRVlkV2cXXu9WVVVnfVmJv0D+xOrjyUmK4bXfvwd9J14+L5/fmv5No5tWD5hOXPHzmXGgBkXejriIiPBmRBCCCFEIx3NOUqYZxhOtk5qjdmDD+J7LJXJt0LC/eNAr4fff7f4uPG58YR4hADg7+p/UWXO6nRq3LgRbG1h4MB6z+8XoNadjVs8DuP+W7C1q+K221plqheclc6Kqb2n0tu/94WeirjISHAmhBBCCNFIx7KPEeEdof7y++8wbx4Z//cAKyNgfzdv8PGB+fMtPm5CbgKhHqEA+LtcXMHZ8ZzjAER4VX9fNm6E/v3Byane86MCVP3i4YwYHI/ew/XXWeH5z0+cCXFOEpwJIYQQQjRClVbFCf2JmiBk/34AXGa+AkBsYSJMmAArVkBenkXHTchNoKNDIERGMn5X0UVV1piYl4i1zppAt0AoLIQ9e85a0gjg4eBBhFcELqk3UWxwZ9KkVpysEBcpCc6EEEIIIRohKS+JkoqSmuAsKQm8vXH28MHPxY+T+pMwaRKUlsLPP1ts3PTCdMoqy+itt4eYGO79chftYk6htWDr/sZIyE0w7/fGwYNQWQlXXnnOa7644QsG532Gmxtcf30rTVSIi5gEZ0IIIYQQjXAs+xgAnbw6qQNJSdCuHaDa6Z80nFTlfB06wIIFFhvX1Ea/U2YFAOXOjixcWE5e4nGLjdEcCbkJtHdvX/2XBPU1PPyc1wzwHcbWNX7ceis4Orbs/IS4FEhwJoQQQgjRCMdyVHBmXnN2WnAW7hlOnCEOdDqYPBnWr4eUFIuMa2qjH5iSD7a2bPryOTyNYHvnZCgvt8gYzZGYl2huVkJiovpa/X05m99+g4ICpKRRiGoSnAkhhBBCNMLxnOO42bvh6+yrOjUmJtYKzlLyUyitKFURh6bBTz9ZZNyE3AQAPOJOQadOOAwYxP1jwXn7HnjvPYuM0VRllWWk5qfWZM4SE1VTlLM0AzFZsAD8/GD48FaYpBCXAAnOhBBCCCEa4VjOMSK8ItDpdKrhR2FhTXDWJhwNTWW5OnSA7t1VY5AmMJYb+ePkH+Y1ZfGGePxc/LA+egy6dMHf1Z8fekB2tzBYudJiz9cUKfkpaGg1mbOEBAgJOec16elq2pMng7V1S89QiEuDBGdCCCGEEI1Qq41+UpL6Wh2cdWjTAahpK8+NN6rNmPX6Ro+z5MgSRs8fzQc7PgAgIS+BCOf2EBcHkZH4u/gDEN8tCHbtgpKSZjxV85iyerXKGtu3P+c1334LFRVw330tOjUhLikSnAkhhBBCNFBRWRHJ+cm1OzWCOTjr6tMVgEMZh9TxsWNV18LVq2vd5/XX4dlnVUPHs0ktSAXg6T+fZn38euIN8Qwo8oSqKujSBVd7V5xtnYmO8ICyMtW6/gJJzFVrzNp7tFelnElJ5wzOqqrgq6/gqqsgIqK1ZinExU+CMyGEEEKIBjqhPwGc0akRzMGZq70rYZ5hRGdGq+P9+oGvb63SxuRkePFFeOstFZyYemecKasoCwcbBzp5dWL8kvEk5yfT2+CgXoyMBMDPxY+d7atrArdssdyDNlJCbgJWOiuC3IIgKwuMxnMGZxs2wMmTcP/9rTdHIS4FEpwJIYQQQjSQqY1+rcyZra0KwKr18O1BdEZ1cGZlBTfcAL//rrJbwOefq+TSrFlw9Cj07q1ePlNWcRZ+Ln4sHb+UkooSKqoqVBt9KyvopIJDf1d/juv00KULbN7ccg9+Hgl5CQS4BmBnbVfTRv8ca87mzAEPD7jttlaZnhCXDAnOhBBCCCEayNRGv6NXR3UgKQmCg1XAVK1H2x4czzmOsdyoDowdC/n5sGkTJSUqMLnxRnjkEdi7F4KCYPx4yM2tPVZmUSY+Tj509u7Md7d8h521HSFpJRAWBg4qg+bv4k9aYRoMHgxbt6oSygsgMbeeNvpnyZxlZ8PSpXDXXbK3mRBnkuBMCCGEEKKBjuccp517O5xsq1vEn7bHmUkP3x5UaVUcyTqiDowcqYKpFStYtEgFJ1q/WczdN5cOHWDePBW7ffZZ7bGyirNo69wWgJs730zBswW0iU9TWbJq/i7+pBWkwZAhqnPk4cMt9uznUmsD6vMEZ99/r5KIUtIoRF0SnAkhhBBCNJCpjb7ZWYIzoKa00ckJrrkGbfkK/vd+MXZ+J1hRPoPZ+2YDqqxxzBj48EMoLq65T2ZRJj7OPua/22lWcPy4eb0ZqLLGgrICigdEqQMXoLSxoqqClPyU2pkzd3f1px5z58KAAWqXASFEbRKcCSGEEEI0gKZpqo2+KTirqIDU1DrBWZhnGE62TjXBGcCNN7IjyZ8j0U44XDmXQe0GcVJ/0vzyc8+pPhpz59aMlVWURVuntjX3OHkSysvrZM4ATnnZQmDgBWkKkpqfSqVWWZM5S0g4a9YsNlYl9+68s/XmJ8SlRIIzIYQQQogGSC9Mp6CsoGaPs1OnVE/4M4IzaytrurXtVtOxEcgdMZiPeQQX6zwOvDKFsRFjyTHmkFeSB6iqxMGD4Z13VMlfQVkBpZWltTJnxMSor5GRrF4N69eDU7kaO60wXd1k82bVbaQVJeapMsZambOzNAMx7ZV9/fUtPy8hLkUSnAkhhBBCNICpGcjZ2uifrkfbHhxMP4hWHSh9c+Iwi3Tj+FfVt4QOGcQ1q49jXQlxhjjzNc89p9rsL1ig2ugD+DidFpwdUWvY1qR05brr4Oqr4Y7+w+G9FJb8ZKOCs9TUmm6JraQxG1CvXAmdO0N4eOvMTYhLjQRnQgghhBANUG8bfag/OPPtQY4xh/TCdAA++ciGKp2Ox9ZcC3360Pv1ufz2A8RlHjNfM2YM9OqlNqiOT88BMDcEASAmhvKgUB5/3okOHWDNGnj5jSJwymLOq73J7jpUndfKpY2m4CzYPVi1nMzPrzc4KyyEjRslaybEuUhwJoQQQgjRAMdzjuNo46iCEKgJzoKD65x7elOQxPR84taNpPOwA4Rc0wn+/BPjR+8y5iSEvfihuQxRp4N331WJpxl3h0OZU+2yxiNH+Nz5SWJi4L33YNQoeOEZJ2zGTaGkyJb//hipmnC0clOQxNxE/F38cbBxqMna1ROcrV2rSjYlOBPi7CQ4E0IIIYRogPjceEI9Q7HSVf/zKSkJ2rQBF5c653b3Va0IozOieer1OChz4flnbNWLOh2Oj/4fs4Y50nv5TrUbdbURI2D+fIjZ7wk//oK7TXXmrKqKnJhMXkqYwsiRap80dSsd/mEGIq5dx5ezdUR3uh3+/rvFvgf1SchLoL3HGW3061lztnIluLmptXVCiPpJcCaEEEII0QB6ox5vJ++aA/W00Tdp49iGQNdA9iXHsPy7UOw7/8WdI2v3jl84sTubo3zgiSdqOmWgNqS+9elVEDeKh6cEsnw5xG89xX+Nz5BX5sgHH6gsm4m/qz/+N8zGwwMeT3kCLSHRos99Pg3ZgFrT1COOHg22tq06PSEuKRKcCSGEEEI0gN6op41jm5oD5wjOQJU2rl0aRFm+O9ff+3dNxq1amHcHpt/hCD16wPTpqvNjteCh67Af+zjr/rTm5psh7KogPuMhHhyXTbdutcfxd/EnWzvOK6/AX2mRLEvrD6WlFnnm86nSqkjKS6q9AbWjI3h71zpv/35IS5OSRiHOR4IzIYQQQogG0Bv1eDp41hw4T3DWzbsX2WunQOAOZtzRu87r4Z7hHCtJoeKJx1SXxV27zK9lFWcRcPVycnNh+3b4MvRNnvf+kte+8KlzH38Xf9IK05g+HboFGXiQz0nelda8h22gtII0yqvKazJnpj3OTk/tobJmOh1ce22rTEuIS5YEZ0IIIYQQDWAoMdRkzvLy1J9zBGclh64DQzgeI2czqN2VdV4P9wynSqsicVA3sLGBpUvNr2UWZdLWuS0uLjDQ9TDT4p/jtWeL8PDU1bmPv6s/2cXZVOnKWPRaLMU4ccu/2mA0Nv+Zz8fUqbFW5uws68369YO2beu8JIQ4jQRnQgghhBDnUVJRQnF5cU1wlpysvp4lONM0WPt9FHgdY+JtTlhbWdc5J8wzDIATVVmqE8jSpebOjVnFWTWdGr/5RgVvkyfXO5afix8AGYUZRA71YT6T2XvCjfvvb/n9qBuyx1lamkoKSkmjEOcnwZkQQgghxHkYjAaAmrLGc+xxBrBuHcREO3LDPTE8Nfj/6j0nvI3aifmk/iTceiucPAmHDgHVmTOntlBeDt9/r9ozniXt5O/iD0BaYRoEBTHWehWvDl3LggXw/vtNetwGS8xTDUDaubeDoiLIzq4TnC1cqILE8eNbdi5C/BNIcCaEEEIIcR6GEhWcmTNn5wnO3n4b/P1hyes3E+oZWu85/i7+ONo4EmeIg5tuUouyli5F0zSyiqozZ6tWQWYmTJ161rn5u1YHZwVpKsMWGMjzwd9x883w/PNqOVtLOVVwCg8HD5ztnM/aqXHBAlXSGBHRcvMQ4p9CgjMhhBBCiPPQG/XAacFZYqIKhPz86py7d6/acPmxx8De/uz31Ol0hHmGcdJwEnx91QZgS5eSV5pHeVU5bZ3bwtdfqzHGjDnrfWplzgBCQtAlJvD++1BZCW+80bRnboj0wnTz+PXtcXbkCOzbd9aKTCHEGSQ4E6KZqrSq858khBDiklYnODtxAsLDwbruWrJ33lGbLU+ffv77hrcJV8EZqNLGQ4fIPbQHgHZFtqqTxt13q0DwLHxdfNGhU5kzUJmrxERCQ1XCbc6cmrjJ0tIK08xr3oiNVV/DwsyvL1igvkVS0ihEw0hwJkQzpOan0ubtNvwe+/uFnooQQogWZArOPB2r15wdPw4dO9Y5LyEBFi+GBx8Ed/fz3zfMI4w4QxyapsHNN6uDvyxlwiG4YfIrqtTxHCWNADZWNvg4+9TKnJGSAuXlzJypbvH66w180EZKL0w3l1Vy+DC0aaOygKht2xYsgGuuMR8SQpyHBGdCNMOCQwvIK81jc+LmCz0VIYQQLcjUEKSNYxsVdZw4AZ061Tlv8WL18gMPNOy+4W3CKS4vJr0wXQVVffrQ7p3ZLPwZKn19YPPmBi3WMu11BqjMWVUVpKQQHAzTpqmGj3FxDX3ahtE0jbSCNPycqzNnR45AZKR5j7OtW1XGTkoahWg4Cc6EaIb50fMBOJpz9ALPRAghREvSG/VY6axws3dTWamSknqDs+XLoVeverf6qle4p+rYGGeojpymTcPo6cq0G8CwYTUMHNig+/i7+teUNZoGr65lfO45VRX5yisNm1ND5ZfmY6wwqsyZpqnMWWSk+fX588HZuSYhKIQ4PwnOhGii6IxoDmUewtbKlqPZEpwJIcQ/md6ox8PBAyudlSpphDrBWWYmbNvWuGDE3E7ftO5s+nQ+XPIkc/qCj2vdZiNnUytzZgrOEhLUa/4qkzd/PmRlNXxu55NemG4em6ws0OvNwVlpKfz0E9xyiwrQhBANI8GZEE00P3o+NlY2TOk5hRM5J6ioqrjQUxJCCNFCDCWG2s1AoE5w9uuvKoF0000Nv2+IRwg6dGqvs2qZRZm42bthb3OOVo9n8HfxJ6Mwg8qqSggOVqWFp3UBuftu1blxxYqGz+18TMGgn4ufKmkEc3C2fj3k5sLEiZYbT4jLgQRnQjRBlVbFD4d+YEyHMQxuN5jyqvKakhQhhBD/OHqjviY4O34cnJwgIKDWOcuXq+VePXs2/L521nYEuwfXZM6ArOIsfJx8GjU/f1d/KrVKsouzwc5Oza06cwY1pZZLlzbqtudkzpy5+tcJztasAQcHGD7ccuMJcTmQ4EyIJtiYsJHUglQmd59MF58uAMRkxVzgWQkhhGgpeqMeT4fTOjV26mRufAFQVAQnXqYRAAAgAElEQVR//lmzl3RjhHuGE6uPNf89syhT7XHWCHX2Oqtup2+i08Ftt6n91/LyGje/szGtcTNnztzczAHrmjVw1VXg6GiZsYS4XEhwJkQTzI+ej6udKzdG3EiEl+qiJevOhBDin6tO5uyMksY//lA9QhpT0mjSN6Ave9P2klOcA1RnzpwbnzkDajcFOS1zBmobtbIyWLWq8XOsT1phGvbW9ipoPa1TY3IyxMTA6NGWGUeIy4kEZ0I0krHcyJKYJdwWeRtOtk64O7jj7+JPTLZkzoQQoqUdSD9AWWVZq49rXnNWVgbx8XX2OFu+HDw9YciQxt97UvdJVFRVsPjIYqA6c+ZkgcxZcrJaaFZt4EDw87NcaWN6YTp+Ln7odLqa4AwVqAKMGmWZcYS4nEhwJkQjbUveRn5pPndE3mE+1sWni8qcLVmifmkLIYSwuJXHV9L7y97M3Te3Vcet0qowGKuDs/h4FfCcljmrqFDNQK6/HmxtG3//Hr496Na2G/Oj51OlVZFdnN3ozJmfi+rsaFoHRkiImtipU+ZzrKxU98RVq8BobPw8z5RWmKbGzcmBjIxa680CA6Fr1+aPIcTlRoIzIRoptSAVgA5tOpiPdfbqTHnMYRg3Dp555kJNTQgh/rFyS3KZ9ts0ADYmbmzVsfNK8tDQVPlePW30t25VXeSbup+XTqdjUvdJbE3eyv60/VRUVTR6zZmjrSPu9u5n3evM5NZbobi4JrvVHGkFaaqcMqa6ciQykspKta5t1KjGr70TQkhwJkSjmT6VNH1KCdDZuzMTtxeqv/z6KxQW1rrm3W3v8vjvj7faHIUQ4p/myT+eJL0wnZ6+PdmStAVN01ptbEOJAUBlzuoJzn7/XW3y3Jwyvju73wnAhzs/BGh0t0ao3oj69LJGqLPubOhQaNMGfv65yVM1Sy9MV+WUp3Vq3LMHDAYpaRSiqSQ4E6KRMgozcLRxxMXOxXysq2sY9x6A4pAgVSuyfHmta36O+ZmfYyzwm1AIIS5Df578k7n75/LUlU9xX5/7SC1IJSkvqdXG1xv1QHVwduIEeHmpCKfaunUwYAC4ujZ9jHbu7Rjafig//v0jQKPLGuGMjajbtVNfz8ic2drC2LHqc8SyZizdK6ssI8eYoz6oPHxY7TQdHMyaNSpjds01Tb+3EJczCc6EaKT0otMWQFfrtSUWLyP8+eQtEBQECxfWuibeEE9aYRpVWlVrT1cIIS5phWWF3P/r/UR4RfDi0BcZ3G4wAFuStrTaHEzBmaejZ51OjQYD7N0LI0Y0f5zJPSZTUVUB0OiyRqjOnJnKGh0dwde3TuYM4I471AbRzWkMklGYocY0Zc66dAErK/74A/r2VfGrEKLxJDgTopEyCjPwdfGtdczz20Wc8NaxLlSDCRPUaugc1RK5uLyYjKIMKqoq1OagQgghGmxt3FoS8xL5aMxHONo60r1td1ztXNmavBWyslTTixZWK3N2RnC2YQNUVcHIkc0f5/bI27GztgOaWNZYnTkzl3yGhNTJnIFqcd+xI7z3HjS1OtSUoTPvcRYZSV4e7NghJY1CNIcEZ+KyZCw3NjmLlVGUUWu9GQcPotu+nV+HBxKTcxQmTlT/WKgu6E/MrfnFaP5EUwghRINEZ0SjQ2fOmFlbWXNF8BVYrVqtSveGDVNpoBZkMKo1Z16V9pCaWis4W7cOnJxUWWNzeTh4cEOnG4CmlzWWVJSQV1q9y3RISL0dhK2s4PHHYc8e1cykKUy/z4KqXFRHyMhI1q1TjSxlfzMhmk6CM3HZKa8sJ3xWOLN2zmrS9emF6fg6n5Y5++ILcHDg+A1XqHb6vXtDRIS5tDE+t+YX46mCU2feTgghxDlEZ0TToU0HnO2czcemH3Plgy8SqGzfDnbtUgFaRkaLzcFc1piiKiLODM6uugrs7Cwz1lsj3mLu2LnmDFpj1NmIOixMZc7qyS7efbdaNvf++02bp6k5VlBqgToQGcmqVeDurvZTE0I0jQRn4rKzP30/aYVp7EjZ0ehryyvLySnOqcmcFRbC/PkwfjztQnuRkp9CQVmhyp5t3AipqSTkJpivl+BMCCEaJzojmh6+PWoOfPYZt7y+lK3B8NfCN2DlStWkY/DgetdXWYLeqMfJ1gm7uOr7V29AnZoKR49apqTRpKNXR6b2ntqka+tsRB0eDuXlajPqMzg7wwMPwLJlcPJk48dKK0xDhw7PeBWkaV1UcDZqVNP2ehNCKBKcicvO1iRVw3Es51ijr80qzkJDq8mc/fGHCtDuuYfO3p1r7jtxoirkX7SIeEO8+RNQCc6EEKLhisqKiNXH0r1td3Xg2DF46CEqrx3NDXdZsclwQLUFXLsWsrPh9tvVAjALM5QYarfR76D2uVy3Tv3VEs1ALKFO5iw8XH09S/T10ENqC4CPPmr8WOmF6Xg7eWOzZy+4unIgN4S0NLURtxCi6SQ4E5edLcmqw9fxnOONXndm6k5lzpytWAGenjB4sDk4O5p9VJW8DBoEr7xC2ZFoQjxC8HHyueDBmbHcSP85/c0BqhBCXMwOZx1GQ6vJnC1eDIDN7K/oHNzb/H7OFVfAJ5+otonz51t8HnqjviY4CwxUaSdUcObtDT16nOcGraTezBmcNTgLCIA774Svv1ZdJ4vLi+k3px9/nDz/DtVphWnqd+G6dTBsGKvWWAMwZkzzn0OIy9l5gzOdTve1TqfL1Ol0f592bJxOpzus0+mqdDpd35adohCWo2kaW5O2YmdtR3F5Man5qY263lRj7+viq1Y9//YbXHcd2NjQoU0HbKxsOJx5WJ08fz7Y2/PkGxvpaR1IgGsApwovbHCWnJ/M7lO7WRe/7oLOQwghGuJQxiGAmuBsyRK48koIDGRwu8HsTNlJeWW5em3iROjXD557DoqKzPcwGlVs0pw9q/VGPZ4OnvD339C1K6Dut3YtDB+uGmxcDNzs3XC0cazJnAUGgr39OesWZ8xQ366FC2Fz4mb2nNrDD4d+OO9YaQVp9CrxUPceMYJVq9S339f3vJcKIc6hIW8n3wJnfg7yN3ArsMnSExKiJZ00nCSjKINbOt8CNL60MaPotMzZ9u2qXf7YsQDYWdsR6RPJgYwD6uSQEFi2DB9DKb1f60Xx6qdIPFV0lju3DlPHsdOblAghxMUqOiMaZ1tnQj1D1bqygwdV6SIwKHgQxgoj+9P3q5OtrFR3i9RUePddALZtg+7dVRVip07wzDPqFo2lN+rxtvNQLeO7qxLLY8dUk0JLrjdrLp1Op/Y6M2XOrKxUU5DY2LNe07s3dOsGCxaobQsANiZuPO9Y6YXpDDtZCUBO1Ch27FCfVQohmue8wZmmaZsA/RnHYjRNa/yCHSEuMFM5n2mx9bHsxv0YmzNnzr7w669q1fNpPYP7+Pdh76m95j1m8np1o2fIezx36n1OrJhE9HPLeP55yM+3scTjNJqhpDo4M0hwJoS4+EVnRtPdtztWOivz9iTcdhsAg9oNAuDuX+5m6LdDGfrtUGbZ7qPi1jvIfftLnn24gCFDIL+kiEFTlxEWpvb16tMHtjRy/2pDiYHOuTZQWmoOztaqOOaiWW9mEuYZxuGswzUHwsPP2/Fj0iQVyK7aHQNAQm4CSXlJZz1f0zTSC9Ppc8QAvr6sSexMVZUEZ0JYwkWSiBeidWxN3oqHgwcjQkfgbOvM8Zzjjbo+ozADFzsX1dJ5xQq0ocP4dZM7Gzeqev0o/yiyirNILUiluBhuG1fF8dgnuKvTcg7ouuMQ8htvvAHTpvWlpKSFHvIcTJmz0ztICiHExUjTNNWpse1p680GDFB7mwEBrgFM8HqTiq2PEvPZy2z/v4XMuPIBbJcuwtN4irc+dWW87y/McvJjVOwtfP9zBqdOQXAwTJum4qyG0hv1RKRVl09WB2fz5kFkpEpMXUyGhwwnOiOazKJMdcAUnJ2jrnPiRPX1yF89uT1SZSY3Jpw9e6Y36imvLKfTgWQYMYKVq3T4+EBfWegiRLO1+Mf3Op1uGjANwNfXlw0bNrT0kI1WWFh4Uc5LWN4fR/8gwimCzZs2E2AfwPYT29nguKHB10fHReNm5cbO+fMZcPQor/i9zEtja1738JoCXEO3j12pKKmguNgNRj/GqNu86D79b571nUjhhHb87+UreeWVGEaNarl9eeqzM3UnAMl5yaz9ay02Vhcmgydan7zPiQuhOT93WaVZ6I16HPId2PHDDwzct4+TDzxA8oYNJCU58tVXYWze/AwAfn5GBvXMJ8FuKQllh5lY4MetyX8Rri3FOdeaCSfg1xcew/Wm6Tz4YBueeaYHDzwQz5QpieedR2llKSUVJXgcSkazsmJzdjZHvtjLnj1RPProCTZubNza5Zbmke8BwCcrP+HqtlcTqGl0LCpi6y+/UN6mzVmva9c5lKRDdzLE9i/W2Kzhxx0/EmwIrvfc+KJ4umWCk6GQw/5B/DannIEDc9i06WiLPFNjyHudaG0W/5nTNO28f4AQ4O96jm8A+jbkHpqmERUVpV2M1q9ff6GnIFpBdlG2xktob2x6Q9M0TZuwZIIW8mFIo+4x/Nvh2qC5gzTtvfe0/fTU7O2qtDFjNG31ak176y1NGz+xXKPbj1rP0fu0Bx/UtOkfLNV4CS2zMFM7Nbyflu6Mtjt2mxYcXKQNGNAST3lur296XeMlNF5Ci9PHtf4ExAUj73PiQmjOz92q46s0XkLblLBJ0/73P00DreJEnPbww5pmba1pLi6a9sormpaWVnNNRWWFdufPd2q8hGb1spU25OshWoo+UdsVgJbv5app+fmapmnaxImaZmenaUeOnH8eqfmp6j1zaE9N69xZ0zRNu/deTXN21rTc3CY/XospryzX3N901+5bfp86sGqVpoGmbdlyzuuG/Hu+Bpq2e2+ZNnbhWK3DrA5nPfeP2D+0GaPRNNC2LU3TQNMWLbLkUzSdvNeJ1taUnzlgj3aWeEnKGsVlY1vyNqBmnUKEVwSJuYkYy40Nvkd6YTq+Lr4ULP2TO+yW4+Wt47vvVOvg//wHfvzBhsgHXyHo7hf47DNw7LwJZ1tnvJ28yb/vLnyLwPqXn7npplR27lRdn1uTqawRpCmIEOLC0DSNWH0sc/bOYdLSSby5+c16z4vOiAagu2931aUxKorvt4TyySfwr3+pSr0XXgA/v5prrK2smXfzPO7rfR+jw0ez8s6VBHq2463xgbjmFMBrrwHwwQeqG/706effFk1vVMvufU6mQ/fuGAyqs+GkSeDu3vzvh6XZWNkwLGRYTVfe87TTN0kJ+giddQU//WjL0PZDidXHqo7GJSVqp+rJk+HVV6F6vdmIeCgLbc/ynX5YW6vt5oQQzdeQVvoLge1AhE6nS9HpdP/S6XS36HS6FOAKYKVOp1vT0hMVorm2Jm/F1sqWfl49oEcPnrn1Aw59qlE5bCiMHw8PPwyvvAK7d5/1HhlFGYRVeDB9292cLA9m4ULw8al9TpR/FPvS9gEqAArxCEGn0+Fy3c0c84KA75YyenQ6zs7w6act+cR1GUoMamE9su5MCHFh3LLoFjp+3JFpv01j8eHFvLrpVUoq6i7Cjc6Mpp17OzxSc2DXLkpunsCLL6p1TV98AW3b1n9/Gysb5oydw6pJq3C1d1XHrhzET/1dVFR27Bi+vqo5yObN8NVX556v3qjHuRRcUjKge3e+/VbFKw8+2MxvRAsaETqC+Nx44gxxqnOwldU5g7PE3ETiy3bTZWASCxfCkOChoEH+I9PUN/qWW9CWLYP//heeeYaM3BSGJUDV1SNZuBBGjVJbfgohmq8h3Ronaprmr2maraZpQZqmzdU07Zfq/22vaZqvpmmjz3cfIS60LUlbiAqIwnH5Sjh0COPg/hzzgpKSAjhwQPURfvFFuPpqSEmpc31ZZRl6o56iOVEs1CbyygNpXHVV3XH6+PchrTCNtII0EnITVAtowNfNn0/7g++hePxTjzB5svr0NSenpZ+8hqHEQIc2HbDSWUnHRiFEq9uRsoPlx5bzUL+HiHkohmUTlmGsMLI5cXOdc6Mzounp1RXuuQdcXPii/F8kJcFbb4FO17hxo/yjeOSqQqocHeDRR0HTuOcetUfZ009DWtrZr9Ub9XTNUv+7qmt3Pv9cbbXWq1fj5tCaRoap/v7r4taBnZ3qgnKO4MyUZbvnLltSUiD/eC+mH3agy3er4NprKfj1Z7q86svnfYH//Y+oR9/EtQx2t59MUpLKIgohLEPKGsVlobSilD2n9jAoeBB88gl06oTtTz9z2wSY/eFdasMag0H98qqsVL+8z5BZlAlxQ5m9YRo3emzm2U8C6x0ryj8KgL1pe1XmzD0EUJ/mrrrShxJHWwJ/+YWHHlKfvn7zTYs9dh0Go4G2zm0JcguSskYhRKv7aOdHuNu789bIt+js3ZmRyw/xx3wduR+9DVlZ5vPKKss4mn2Ux3/Pgy1byH//K17/zJORI5vWur6Pfx8yXSD2kUnwxx+wfTs6HXz5pXofruct38xgNNCjunfTX0UDOHHi4s6aAXT27oy/i39NaWOHDufc62xd/Dp8nX359+Qg2rSB914v591VFewJc6TqhwWMz/uKuMJkEl97it+GBjA8uoAqHfwQNxAnJ7jpplZ6MCEuAxKcicvCD4d+oLSylNuLQ2DHDnjoIVwc3Ah0Day1EfVnOb/z/c2h8MsvxH33EVVazWKEfTF6bBctoRPHmf9eBlZn+a+nl18vdOhYF7eO/NJ8c+YMwM0niA0D/fDZsIHukZVcdRV89pmKB1uDocSAp4MnoR6hUtYohGhVyXnJLD68mPv63IeLnQsYjdi9/hZXJeoY9/E68PdXC3hXrOBoxmGGnqhg2MLtMHUq76eOJzsb3nijaWP38e8DwG9X+YOrq6qLBDp2VJV6S5bAihX1X6s36umeAVVOzrw9zw8vL/M+2BctnU7HiLARrItfp36PnWOvM03TWBe3jqtDr8bZWcd/ntZY/ZcDe8oGced1Rh5a/QirY1cz69pZvDX6f9zwVzIVj82gdPJUflrhwE03gYtLKz+gEP9gEpyJS56p3PBsKqoqeH3z6/Tx78OAZXvUKvApUwCI8I4wb0SdUZjBjKcKmbLyDzx02UROmYa9SwGjR2u8+CLMmBKCfbk1i73vxe2us39M6GrvSievTiw9uhSAUI+a4CzANYCdQTqsS0shMZEHHoD4eLX5Z2vILcnFw8GDUM9QyZwJIVrVp7s/RUPj4f4PqwPLlkFuLr++N42eD0Deo9Ph77/hppsI7XcNP/wMZR1DyZw5i/feUwFRv35NG7uNYxtCPELYmX8Y7r4bfvrJXFP+1FPQrRs89JAqoDiT3qinRyYsDpjB2nU6XngBHBya+E1oRSNDR5JdnM2hjEMqOMvOhvz8OufFZMeQUZTBiFCVknzYbwl+pPFMwFec8IIv9n7Bvb3uZXrUdHWBlRU2H3zI2nFz0eulpFEIS5PgTFzyXt/0OhGfRJBXklfv6wsPLeSk4SSvdZuB7scf1S/m6hZbndp04ljOMTRN496XN1Cx8WkGRXlw2w25PMIn9HVZREJKKa+9BknHXVhcNZGAfw8DW9tzzqmPfx+S8pIACPEIMR8PcA1gj2uB+ktMDDfcAPb28PPPzf42NIjBqDJnIe4hnCo4RWlFI3ZhFUKIJioqK2L23tnc0vmWmvfEb76BkBC63vEw0X6waFJPiI+nctEiYpyMOGhW2C5eyhsfOWM0mhstNlmUfxR7T+1VLRpLS+HbbwH1dv7VV3DqlNpQ+oUX4M9DB1SnQkBfnENIhhuPn3qK3r1VEHcpGBGmgq118evO2bFxR8oOAAa3GwxGI05P/pvn289nZ1IHnJNvIco/ik+v+xTdGQv9fvgBvLxUMxAhhOVIcCYueX9n/U12cTYf7/q4zmuVVZW8tvk1evr2ZMzGVPUL+eGHza9HeEeQW5LLso1xrP7oRny7HWb9H87MXRHOC/ceZHvmdH7zu4K82Cx+G34jV9msweHBR847J9O6M6BWWWOAawDbXao/mj16FFdXGD0ali4FtXVgy6msqiSvNA9PR0/znBLzzr8BqxBCNNf30d9jKDHw2MDH1IHERFi7Fu69l85tI2nn3o7fY38HW1uWRGoMmFzM77t+INm9J59/DvfeCxERzZtDH/8+nDScJLdjMAwapEobq/voDxgAO3eqflCvv64xKqoT4WMXs3jf72jpaXxofJl0oztffAE2Ns38ZrSSILcgOnl1Ym3cWrXmDOpdd7Y7dTdu9m509Oqoyv6zs7n/g660bw+h+xbw511rcbR1rHVNQQEsXw533HHezyqFEI0kwZm45JkyVO9vf5+C0oJary06vIiUtON8Yn0jus8+U795IyPNr0d4RUCJK5Mm2IF9LksW2Zl/8brN/pZ3bmhDyKZoXAZ2Y+TmNfzYxxZHv6DzzikqQAVn7vbueDh4mI8HuAaQ4wQl7m4QEwPAbbdBcvI5O/hbRF6pyix6OniaP7mWjo1CiNYwa+cs+gb0VU2ZAObNU1+nTEGn0zEmfAxr49ZSWlHKq5teJdInktu7juOll1RnxhdfbP4cTB+a7U/brzp6xMbCX3+ZX+/bV1UxvLvsd+jyM6UbH+OOoT3Z9HkfPuYRpt94iv79mz+P1jQoeBD70/erlCDUmznbfWo3fQP6qm1WNm0CnQ77qwfx4ovw9wFHPnrbg6SkmvNLSmD2bDAapaRRiJYgwZloNZ/t/oyr511t8fsm5yUT5R+FocTAp7trNg6rPJVK2O33k/sWDJ72muoE9vzzta4Nc4+AX77DmOnP1U/PZnBkx5oXbWyInXY7Qx5yRAsMQFdVxeJrAho0p95+vYHaWTNQwRmAPrAtHD0KwI03qk9ilyxp9KM3imkDak9HT/M6OGkKIoRoaemF6cRkxzCx20RVGldVpUoKR4yA9u0BGNNhDAVlBTz959MczjrMzCEziTlixXffwSOPQND5PxM7L1NTkH1p+9SnYl5e8Pnndc7bV7EAr0mPs2GLkbaBRmL2v4g32bzxvmOdcy92YZ5hpBemY3SwUfuVnRGclVSUcDDjIP0Cqhfzbdqk9ghwd+euu+Cqq+Dll9X/Td27qzV/bm7w5JPQtavaUkAIYVkSnIlW82fcn6xPWE9RWZHF7llSUUJGUQY3d76Z6zpex3vb36OwrJCC0gJW/nskfU8Uc/Lem2DNGrUY+uqa4LCsDJ6ZHgrHboYxjzFr+rg69x8RNoKdnkXs+vlj7ny7P0Xh7Ro0L3cHd7r6dKWLd5dax03BWXqAhzk48/SEkSPVJ7YtWdpoKFHBmYeDBwGuAdha2dZuCvLVV2dvVyaEEE10IP0AUBMcsXGj6oQ0dar5nBFhI7CxsmHWrllEeEVwR9c7mDlTdQF85hnLzMPH2Ydgt2D2pu1VHT2mTlW1eamp5nMqqipYdWIV13e6nqGDHEk7Esac/i+zrM1deIa3scxEWpHpg7jEvMR6OzYeTD9IRVUF/QP7q1+K27dj2sDTxgY2bFBFHu+9B35+qtHlk0+qXi5btzZ+vzkhxPlJcCZaTaxe1bqfNJx9I8zGSslXm0UHuwXzwlUvkF2czVN/PMWVn0VxxZ9HOTmkKxFzflErlk/r9Vtaqjp/LVumI/zOWdx1fz5d23atc/+rQ1UwtzZ5I9H2ufi6+DZ4br9P/p2Pr629Ds7fxR+ARF9H1Smsel+f226DuDg4eLBxz98YuSW5gCprtLaypp17u5rMWWoq/PvflqkdEkKI05iCs56+PdWBr78GDw+4+WbzOW72buaSx5lXzWTLZmuWLVMbRHt5WW4uffz7qMwZwAMPqCzepzUVF1uTtmIoMTC201gArIoLuS9tLlf0t7bcJFpRrRL28PA6a852n1L19P0C+sG+fapWccgQ8+s6HXTuDE88AX/+qapA33hD7WtW3VdLCGFhEpyJVlGlVZmDM9NXSzCtN2vn3o6BQQMZFT6KL/Z+wZA9mfgUQ8TMD+p0mNI0FZj9+qvaY+z4/If55qb6d4L2dvKml18v1sWvI6MoAz9nvwbPLcgtCC+n2v+qaOvcFiudFbFtq1dQV2fPbroJrKxatmvj6WWNQO12+h9/DOXlEB0NefV3vRRCiKY4kH6A9u7t1XtPUZHqgDR+PDjWLhN8oO8DXN/xenrbTuC221QPixkzLDuXKP8ojuccJ780X63DuvVWVdpYoNYrrzi2AjtrO0aFV7cgfPJJSEmxXPqulZlK6+Nz46FLF/Usp06ZX999aje+zr4EuQWpkkaoFZwJIVqfBGeiVZwqOEVJRQkAJ3JOWOy+yXnJgArOAD4a8xGP9H+EDxM6q08JR4yoc82vv8Jvv6kyjQcfBCudFdZWZ/9UdEToCLYmbyW3pHGZs/pYW1nj5+LHYa/qXaergzMfHxg6tIWDs+qyRk8HFZyFuIeozFlBgepaFhKiPkXevr3lJiGEuOwcSD9AL79e6i8rVkBxMdx5Z53zJnSbwJzhv3HDdTZYWcHvv1t+c+Mh7YegoTZdBtQmZ7m58PXXaJrGiuMrGB4yHFd7V1i5Er78UgVoQ4dadiKtxM/FD3tre/VeP26c+nTyu+/Mr+9O3U2/wH7qQ8xNm1SarG3bCzdhIYQEZ6J1nJ4ta4nMWZCbWi3e2bszs4KnYbdtp9rLxqr2j7imwUsvqbjt0UcbNsaI0BGUVZYB6hddcwW4BnDUuVh9alzdsRFUaWNMTK1DFlVf5iyzKJPSzz9V2bJvvlGLDDZvbpkJCCEuO8ZKI8dzjtcEZwsXqu4egwfXOTc/H667TlV7r1pVszWXJQ0KHoSngycrjlevrx0wQM3lgw84lnGYWH0sYyPGqkn861+qC8arr1p+Iq3ESmdFe4/2KnPWsaPKin3zDWgaBaUFHM0+qkoaKythyxbzejMhxIUjwZloFaaALMA1gFiDZYMzX2df7G3saw5++SXY2amNcc7w6yBbbroAACAASURBVK+wfz/MnNnwvWqGtB+CrZUqQ/R1bl7mDNT3IKsiR23aU505g5rlFy3Vk8NQYsDO2g5HG1VKFOoRik0l6GZ9pD4VHjYM+vRRv6CFEMIC4ovi0dBUB1u9XqXDxo+v88FZaqrq13ToECxerNratwRba1uu7Xgtvx3/jcqq6gqGp56CxEROfPkmAGPbDoG77waDARYsAHv7c9zx4hfqEVqzvvjee+H4cdi2jb1pe9HQVHB26JD6kE6CMyEuOAnORKuI1cdia2XL8JDhFs2cJecnm0saASgsVCUb48aBt3etc0/Pmk2e3PAxXOxcGBg0ELBQ5swlgOzSbFU+clqaLDAQevdWJZctIbckFw8HD/MavBCPEO44DHap6eofJ6A+Qd65U3VMEUKIZootVO/3vfx6qbVm5eUwcWKtc/bsUS3ajx2DX36Ba69t2TmN7TSW7OJsdqbuVAduuAE6daLTN8t56WQ7gvpeDX/8AR9+qDJnl7gQj5CaPS3HjQNnZ/j6a3al7gKgX2C/mvVmEpwJccFJcCZaRaw+ljDPMCK8IkjJT8FYbrTIfZPykmoHZwsWqNqYBx+sc+6KFSpr9sILDc+amYwMGwnUtMJvDm8nbwoqCqjq3BkSE9X6i2o33gjbtqlGjpZmKDGY15sBhLq14+mtoA/xq/nX0JAhKjDbu9fyExBCXHZiC2PxcPBQ79MLF6rSuj59zK//9pt627GzU+99N97Y8nMa02EMNlY2rDhWXaZgZUXOg/cQkVjEi98nqQ/O9u6t9/fIpSjUI5QcYw4FpQVqEd/48bBoEYfithPqEYq3k7cKztq3h+DgCz1dIS57EpyJVhGrj6VDmw50aNMBgDhDXLPvqWkaSXlJBLtV/zKpqIB33lH1MGfsjGnKmnXoAJMmNX6sxwY+xuJxiwl0C2z2vN0d3NHQKOnQXk3sRE2DlBtuUD05Vq9u9jB1GIwG83ozAN+5i+iZAa8Ohb+zj6iDg1Qra1l3JoSwhNjCWHr59UKXlgbr16usWXX2vrRULQ2OiIBdu1ovSeXu4M7Q9kP59fiv5mNP+B7gmyhrcr78sGYj5n8IUzt9c2nj1KlQVITv6s0qa6Zp6j1fsmZCXBQkOBMtTtO0OsGZJUobDSUGisqLajJnS5aoDTaffbbOzpgLF8KBA/Df/zY+awZqD57bI29v9pwB3O3V5jD5IdVZuNNKG6Oi1EafLVHaWCtzFhuLbuZMskdcycKuVfSf05/vDn6n2kZ27izrzoQQzVZZVUlcURy9fHvBTz+pIOC0ksbvvlNd3d99t/UbBN7Y6UaOZB0hVh/LjpQdfHf8J+LfeQ6vaTP+cTsr12qnD3DllVR0DGfcphzu31GuulFlZkpwJsRFQoIz0eIyijIoKi+yeHBWq42+psFbb6nA4rSNTUFtq/P00yrwaUrWzNLcHVRwlhPspf4RcFpTECsruP56tWa+vNyy45ozZ1VVqguZnR3e835i/wMH6B/YnynLpvDm5jdVjdHWreo8IYRoohP6E5RWlar1ZgsXqmxU586AKnR4+2211qyeHU/+n73zDIvq2sLwOzB0EJCOKMUuWFHBil3sMTExsZuYbnKTm2ISc9PMTS+mm2tMs8caTdTYu1gQpSgqUpQO0qUz5/7YDEXAAkMR9/s8eUbO2bPPPpMp5ztrrW/VOxM6ivzJLRe28OI/L+Jo7sirA15t+IU0AFUiZyoVEZMG4RMHIz7fJIr+5s4VPd8kEkmjI8WZpN7RCrF2LdthbWJNS5OWOhFnWhv91patRR7g2bOwYEEVF7BPPhFOYIsXV9nVKGgjZxnkg7t7Fe/88eOFaZaug1dlkbMlS0TazhdfQKtWOFk4sXvWboa6DWVZ0DJhCpKeDmFhul2ARCK5pziTeAYA31Rjkbc4c2bZvnXrRKLDG280TqDKw9oDL3sv3j/4PgGxAfx32H8xN9RxU7Umgp2pHaYGpuWmIMDvQ6x5eKo+ueeCRe3zzz9Dy5aNuEqJRKKlCVyqSpo7WiHW3twVkpJo37J9ZTv9mBhYteqOIzVacdbGsg18+KEoZL6hsemVK0KcTZ1abVudRkEbOcssyBR3kStEzgBGjBDOzVu3Vvfs2qEoimiiXWwsBOyoUZVaDaj11IzvMJ7L6ZdJ7NFebCxVh4oC+fm6W4tEIrk3CEoIQq1S027538Ih8NFHAfFV/8EH0KULTJzYeOub0GEC6fnp9HDswezusxtvIfWMSqXC3cq9PK0R+CfuAIn+AzHt3LXZpXFKJHc7UpxJ6p2ItAjUemrc3/saHB1Z8UkEgzcGisKqyZPBw0PkG/799x3NezXrKob6htgHXRRC4uWXheVXBRYsEI+ffKKrs6k72shZZn4mdO4ses6UlJTtNzeHoUN1W3eWXZiNRtHQKTpHtBt46aUqP8h+rn4A7CUSnJ1h/36uXYP+Q7Kwccy7UUNKJBLJTTmTdIbeJS7or1krbgZZWQHiqz40VJQHN2Y2w8NeD2NuaM7i0YvR19NvvIU0AG5WbmVpjddyrxGUEMRw90bIJ5VIJLdEijNJvRORFoGncRv0fl8OfftihgH/2ZwuPJMPHxYFYebmsG3bHc2rdWrU+/4HkY4xb16l/UuWwJo1Yvo2bWqYpBGoEjnLzxchvgpMmCBMHC9c0M0x0/PSAXC7XOrR7+1dZUwPxx60MGrBgZiDMG4cZ/64QO82SZw4YkxuQR7jJyikpelmPRKJpHmjKApBCUE8G6gPhYXw/PNl+z79FNzc4OGHG299AN0cupH1WhZ+bn6Nu5AGQBs5UxSFfdH7UFAY7iHFmUTSFJHiTFLvXEq7xNwLJsKZ46uv2LP+U9o/B7Erf4CrV0VK4siRQpwpym3PeyXzCu2MnUX+35QpYGoKCHvmxx8XLWr8/cujZ02FSpGz9qUphBXs9EGYgoDuomfp+UKcOV2MF71sbGyqjNHX02dgm4EciDnAX/7f0t/gJEW5xexjIFO6T+DqFfEy69qoRCKRND92Xt5JVmYKEw/Fi0La0u+6uDjh2j5vXu2cc3WN6h5J6XOzciOrIIv0/HT2RO7BwtCCPs59GntZEomkGqQ4k9QrWhv9+w6lgKcn+PjQrmU7ImzgbO/WYGwsBo4dK6JH587d9txXMq8wLkJPiL6pUwFhy+znBz/9BAsXCnFTqtmaDKYGpuir9EXkrAZx5uoK3brpUJyVRs5szkdXGzXT4ufqx4WUi8x/WY92nQx4f8028l1Psu74Ud58+xz79sGzz96RhpZIJPcYeyL3cN/a+3j1igstsvLghRfK9v35p3icPLmRFnePorXTj86IZnfUbvzc/DDQN2jkVUkkkuqQ4kxSr6TmpuIWk4XrxWQRzlKpqtjpx2bF8pbRUfGE20xtLNYUE58dz9DjyaJBjp8fISHg4yNMBjdsgPffB/0mWEagUqkw0zcTkTMnJ1Eof4M4A3Gz+dAhYZxYV9Lz07HMA5OY+JuKsyFuQyBqODFRal5/HbawnUWlGT/jLNfw+uuwdKloi5ORUfd1SSSS5sX+6P1MWD2BLhYevBXUghwPDxg2rGz/pk3QoYMot5U0HFo7/QPRB4hIi5D1ZhJJE0aKM4lO0SgaZm+ezX/2/qcsavZ4IJQYGpTZKNuY2GBpZElEWgT5xflMXjuZRZG/kNHBFbZt4/Jl4S4fEwMpKdWbOCZkJ2CcX0Kn4xEwZQq79uozYIAYe+hQ02/XYq42F5EzlQratatRnJWUwD//1P14GfkZ9Eoo/eMm4qyXUy/0g57BuEUOI8flsD1iOzZ+/uSpwfjocf77X+HAv3WrmOb06bqvTSKR3P3kFObw8eGPmfz7WF4PseTEx+moQ89x5eGHy8yH0tJg3z7x/XyPZBM2GdytRORsWdAyAEZ4jGjM5UgkkpvQBDK+JfXNibgTZWlt+nr6DGg9ABMDk3o51g8nf+D3s78DcL3oOt6WnZkRDNcnjKZFaQ8VVWn07FLaJZ79+1lOxZ9CT2PE5zbT2b1/AgHtKs9pbg5du4o0P3d34f2RosnBa/c03i9oz8ULz7BurLBl/vtvcHGpl1PTKWZqMyHOQKQ2nj1bZUzfvmBnJ4RQXQvn0/PS8b4NcZaarEZzfgJmfivYc8WE/OJ8nuz/PMdcduB5MhSVCl58UUQop06F/v0hMFBkrEokknuDnMIcjl49ilKa3xycFMynRz/FPDaF8FXGOKQmwoAB8PtykvX16VL6vL/+EjecZEpjw2NtYo2lkSVhKWE4mDngaSe/tCWSpooUZ82cs4ln8fnJp9K294e+z8LBC3V+rOiMaBbsXsCotqPoZNOJLwO+5F8XWzK9AAqffq7S2HYt27Hx/EaKNEXMsfmBVW/fx/tpjrTjEp/PDsZpdDfy8oTr+6VLEBwMa9dWTKXrDKzkBBraRKh45BH49lto0ULnp1UvlKU1ghBnmzdDcXGlCnl9fVGKt2VLlV13THp+Or0TVChtWqOyta1x3K+/glKi5lqXD/nhlBOO5o6MajuKD9oaMGRvgsixtLamf38ICBCt5datk+JMIrmXeGf/O3x+7PNK20Z4jGDVURV2uUdEuH/kSBEe27+/bMymTdCqFfTu3cALlgAitfFs0lmGuQ+7Z4xQJJK7ESnOmjl/XxK9w3bO2Im5oTnTN04nKDFI58dRFIXHtz6OSqVi6YSltG7RmhJNMQ8v/Z4oOzXuw0ZWGt+uZTuKNEX4qp5n48InMTG7hmraZML/2o+++gF45KdqjgG5uSI15uu/fmTa/K9o+/hwWiz5RufnU99UiZwVF0N0tEhxrMCECfDbb3D0KAweXPvjpeel0ydBhWpwzVEzjUbUk/X0zSTI9iIHYi7yTO9n0NfTJ6yzDXp7EkXrgwkTAHGR1bcv7NgB77xT+7VJJJK7ixNxJ+jh2IPvx34PgJWxFZ2TSmB2t/Im9zdw/brQbI891ri9ze5l3K3dOZt0VtabSSRNHPkV2czZEbEDbydvRrYdSb/W/ejh2IPQ5FCdH+fnoJ/ZHbmbT0d+ShvLNqhUKr4xeQDfODg/w79KgcGEDhMYrP435774EltbFR8uP0ZBh81k+vWpYqmvKAq7Lu9ixPLhjF3nx08Rb+MQ8gM9NedpMesRnZ9LQ2CmviFyBtXWnY0cCQYGd+7aePTqUQb9MoisgiwA8q8l4XFNc9OUxj17IDISXpxvholapL1O6TIFgATPNhQa6MGBA5WeM2YMnDgBqal3tj6JRHJ3olE0nEk8Q3+X/vRr3Y9+rfvR2a6zuENjbg4vv1zt8/75B/LyZEpjY+Jm6QbIejOJpKkjxVkzJiM/g6NXj+Lfzr9s24zTJZiGXSS/OF9nxykqKeLlXS8zxG0IT3g/UbZd9dFH4ODA2A/WVXmOaboPwZ99jk1LPfbtg3G9uwNwuqcDJCTAd99BfDxHrhxh8K+DGbViFJeuXSKvKI8fty9i9IazJFobgK+vzs6jIakSOYNqxVmLFqI1wNatdzb/4oDFHL5ymPXn1gNgd+Gq2HETcfb996L92UNT1AxsMxA7UzsGuQ4CwNrKiWBX42rFmaLAzp13tj6JRHJ3Ep0RTXZhNj0ce5RvDAoSFrn//ne1PRRBpDS2bFm3DABJ3Xiy95N85f8Vrlaujb0UiURyE6Q4a8bsidxDiVJSLs7Cwrj/ky0cW6qQ8vl7OmtWFZEWQUZ+Bo/1fAw9Velb6tQp2LVL/Fhre5mVkpsrzCRMTGDvXmjTBlq3aI2zhTPrPfKF68dzz0GrVtj1Gsi4dWf5n9/nXHruEidGryduc1u6ZBmS98M3d21+jLm+OVkFWWgUjWgFYG5erTgDkUUYHg4REbc3d2Z+JlsubAFgRfAKAJwvJYqdNYizLVtE2dvzz4ORESwZv4SdM3ei1hOZzw5mDhxwRdgzZmaWPa93b7C1he3bb29tEonk7uZM4hmAyuLsrbfA2lq4BVVDXp64wTRhQtNoPH2v0sm2E8/7PN/Yy5BIJLfg7ryyldwWOyJ2YGlkia9LaXRp9WoUPT0OtYHWr30I06dDdnadj6NNk/Sy9yrf+OGHYGUFTz1VZfyrrwqr/N9+Azc3sU2lUuHr4svuzCAhUgID2fvsWK60gNe2Z/P4lA8wev9DGDgQ/eQU9HfvxX3qk3Vee2NhpjZDo2jIKcwRKZ/t29cozsaPF4/a5q23YsP5DRSUFDCu/Tj2R+/nauZVPCLTSbUxFfaPN3DtGjzxBHTvDq+9JrZ5WHtUuvhyMHdge6tcUZh25EjZdj09GD1apCxV1/JAIpE0L84knkFPpVf+fX/ihMi7fuUVsLSs9jnr14t7OrNnN+BCJRKJ5C5FirNmiqIo7Li8gxEeI0T0Q1GEOBs2jHGz1fwzd7CwP3zhhTofKzQ5FD2VHp1sO4kN4eEih+XZZ6vYJ27bJjIWX3xR1FNVxLeVL5fTL5OSn4bSsydPdrzIe/8ZBMePQ58+8O67UFgoUusGDKjzuhsTM7UZQOW6sxpCYx4e4nQXL4b828hGXRG8gnYt27HYfzEKCqtDV9PpSi6x7e2rHf/cc0Kg/fYbGBpWP6eDmQNHXUAxMKiS2ujvL/rRBQbeem0SieTu5kziGTrZdipvx7JxoyiMnT+/xucsWSIaTw8Z0jBrlEgkkrsZKc6aKedSzhGbFVue0njyJERGojdtGh3tO/PtyBYwY4YQUcXFdTpWaEoo7Vu2x1hdmr748ccilfFf/6o0LjkZ5s4VPcs++KDqPNoI3/G44+yP3i8aWPd6XFgCbt8uUuoCA0WI5y7HTL9UnFWsO4uOhqKiase/+y7Exgo3xZsRmxXL/uj9zOg6g3Yt29HPpR8bj/9Ku5QSkju1rjJ+wwZYvVpkJd3sZXUwdyDPEHJ7dKkizkaPFsG/HTtuvjaJRHL3cybxTOWUxsOHRbq0hUW14y9fNuPoUZFEId3bJRKJ5NZIcdZM2REhrpRHtx0tNqxaJcIikyfjZe8lUhEnThR9q44erdOxQpND8bQvbXSVkAArVwq/5BtS6F59VaS2rFpVpQwNAG9nb/RV+gTEBrD09FKsjK3K3AIB6NlT+Lc3A6qNnJWUQFRUteOHDROF9B98IGr2amJ1yGoUFKZ3mw7AjG4zMAo5L47lWdmmPy9PBDe9vcvTGWvCwcwBgKTenUU9YYV0WDs7UXsm684kkubNtdxrXM26Sk/HnmJDfr648XeTTIatW50xMpIpjRKJRHK7SHHWTNlxeQeedp60tmwtLvrXrhUdja2s8LTzFI5bfv2EYNuypdbHyS/OJyItAi+70vqD778XkbgbomaRkbBiBTzzDHh5VTMRYGpgSnfH7my7tI0N5zcwo+uM8tSZZoa52hzgthwbQdxxXrQIEhPhhx9qnndFyAp8WvnQrqUQYg95PkTfBPExz+/WpdLYVasgKQk++0xkJd0MB3Mhzi72bSfeTxs2VNo/ZozIPk1Lu/k8Eonk7uVs0lmgghlIYKBINR84sNrxOTmwa5cDU6cKp0aJRCKR3BopzpohOYU5HIw5WJ7SeOCAuKp/RPQE0xZyn8u/KkIydRBn4anhaBSNmDMvTxQXTJhQpZnyxx+Dvn6NLXDK8G3lS1BiEIUlhTzu/Xit19XUKRNnt9HrTMvgwTBiBHz0kbjouZHgpGCCk4KZ0W1G2TZbU1vGZTsSawHGrcrtkxVF1LD16CGs+m+FNnIW2r6FKB755ZdK+8eMEYYg0lJfImm+aJ0auzuU5kBrzYH69692/OrVkJurrs4XSiKRSCQ1IMVZM+RQzCEKSwrLUxpXrxZW7aW2f1pxFpocKoTUpUtw4UKtjlXJqXHVKtGN+AaTkdhYcS3/2GPg7Hzz+bR1Z31b9aWbQ7dareluoErNma2tcDq7iTgDET1LTYVvv626b3P4ZvRUekz1nFppe68ECHQuj36BaDgdGir+V91OHUgLoxYY6RuRnJsiCgcPHqy01j59hDnn7t23nksikdydBCUG0cqiFXZmpSnrhw+LG0v2Vc2GFEVE+T08cu7WdpQSiUTSKEhx1gw5FX8KFcKansJCkYI2aRKYmgLgbu2OidqkXJxBraNnocmhGOob0s66rQjFdOtWxZLr00/FD/Wrr956vsGug1HrqZnfp2bnr+ZAlZqzW9jpa/H1hX79hI/LjcRlxWFjYlN+4QSQnY1FdAJe/rMY2KY89WjxYnE99fDDt7delUqFg7kDSdeTYNYs4aH/669l+/X1YehQIc501D7vjghJCmF3pG6U4cGYg5yMO6mTuSSS5kQlMxBFEfXKNaQ0bt8uelNPnBgvjUAkEonkDpDirBkSmBBIB5sOWBhZiP4z6ekwbVrZfj2VHp72noSmhELr1sJoow7irJNtJwz2HxShmBdfrBSKSUqC//0PZs4s72l2M1ytXIn/dzwzu8+s1XruFoz0jFDrqcsjZ3Bb4gxE7f2ZM1BQUHl7en461ibWlTcGBaFSFNqOfKisQfjFi/D336L+z8jo9tfsYOZAUk6SCH/6+wvv/ZKSsv3Dh0NMjKgvbGjeOfAOMzbOuPXAW6AoCtM2TGPB7gU6WJVE0nzIL87nfMr5cnF24YLowVGNGUh+vmho37EjjB2b0MArlUgkkrsbKc6aIacTTtPLqZf445dfxMX0qFGVxnjZexGWHCb+mDBB3AFNTb3jY4Umh4qUxhpCMZ98IoJ3t3IDrEilyE8zRaVSYWlkWR45AyHOrlypqrpuwMdHvKZnzlTenp6fTkuTG6ruT58Wj97eZZu+/lr4wDz99J2tuSxyBvDooxAXB7t2le0fMUI87tlzZ/PqgquZV0m6nsS13Gt1micsJYy47DjisuN0tDKJpHkQlhxGiVJSLs4OHxaP1UTOPvsMLl+Gb74BA4NGCKVLJBLJXYwUZ02IgNgANIqmTnOkXE/hatZVvJ28IT5edH2eNQvU6krjvOy8SMhJIPX6NTKGTiZM04mAr08QEAABAeKm6K3S07IKsojJjMFH1UYc54knKnnk79sHX34pSpQ6dKjTaTVLLI0tq0bONJpbhp58fMTjiROVt6fnpWNtfEPkLDBQiHNHRwAyMkQ24vTp1ZaJ3JSyyBkIQW9rCz//XLa/QwfR6aAxxFl8djwA51PP12kebQuKhGx5t18iqYjWDKRMnB05Ir4Dbvhyj46G//4XHnwQRo5s4EVKJBJJM0CKsyZCSFII/Zb1Y0XwijrNczpBREp6OfWC5cvFxf7cuZXGaDSQeXYo/HyQNvZWWA/tgRdh9Fs0ln79RE1Tp07g5CQCYcuWif5kN3Iu5RwAQ85mCCVX6gYJIp1x2jSR1rJ4cZ1OqdliaXSDONN2gT527KbPc3ER/2+OH6+8vdq0xsDASlGz5cvh+nWYX4uSPgczB5KvJ4sbCIaGQuH9+adIbUJksw4fDnv3ivdYQ1GiKSExJxEof0/WFq04yy7MJqewGktMieQe5UziGSwMLfCw9hAbDh8WKY03FJS98IIoSf3880ZYpEQikTQDpDhrIgQmBALwz+V/dDJPT8ceIqoxcGClO5tr14o+Y4ue6QXZzvhODOWzz2CN/69sU09k+x/ZbN8OS5eKNLVDh2DePBF8mTdP9BvVonVqbL8/RKiwzp0BcWE+c6aI0vzxhzCKlFTF0viGtEYvL1EDuGULydeTmb15NudTqkaCVCoRPasizm6MnOXkQHh4mThTFNHpoG9f6NXrztfrYO5AiVJCWl5pM7Np00R+ZYVQ2YgRIjs2OPjO568tydeTKVFE7VtdxFlOYQ6HrhzCydwJkNEzyd3JqpBVfH/ye53Pey71HJ72nqJ2NSkJIiKq1JutWiXu17z1lvgqk0gkEsmdI8VZEyE4SVzN7o3ai1IHu7vTCadpa90Wq6Dzwvnh0UfL9q1ZIyJhajWsXKnQ4qW+dJj+Ay+9BFM/7MGY4q34J/+Ov78QYitWCBv848fFdfiaNeLC/rPPxHyhyaG0KjLB+HAA3H9/2R3U998XpUhffw1du9b+NWnuVImcqVQwcSLKzp2M/Wkov5/9nc3hm6t9ro+PuDYqDVqhUTRk5GdUFmdnzghFVirODh+Gc+eodc8hba+zstTGHj1EBC0wsGzM8OHisSFTGyvWh9VFnO2L2kdhSSGzus8CICFHijPJ3ceXAV/ywaEPdD5vZHokba3bij+OHhWPFcTZyZOiXcrgwcIXSiKRSCS1Q4qzJoJWnCXmJNbpAjMwIRBvZ28RNTMzE4n/QEiI+OEcOBBOnYJp01T4tPHm6NXSH9kePYRrY4UaIhB6oW9fEUmLjdMwZFwSr7wCH3wgxNnjcY6oSkpg8mQUBd59F95+W2S8zZtX69O4J6gSOQOyRg1BlZdHm1OXMDc058K16vvP9e0rHrWRzKyCLBSUymmNWtFUKs6WLBGt1KZWboN222j7pJWZghgaitYJp06VjXF2FimxDSnOtPVmXey61OmzsyNiB6YGpjzk+RAgI2eSuw9FUQhPDScuO470vHSdzVtUUsSVzCu4W7mLDQcPCqvX0u+W+HjRrcXRUXRuMTTU2aElEonknkOKsyaAoiicTTrLCA9hd1fbfk1peWlEZ0Tj08JT5C8+9BCYm5ORAZMniwvzP/4o/+H0c/UjJDmE1NxSl8ZHHxXufmfPVjv/7riN7O/VCtNeG1m4EAJW+DMuTAMuLpT07M0zz8A778Ds2cIkUva2uTk3Rs6uF15nxJVFZBvCV8Uj6ePcp0Zx1ru3eH21qY3aVMNKkbPAQFGc5uRESgqsXy+8YUrb3d0xVSJnIC7OAgMruceMGAEHDoiMx4YgLktEzka4jyAuO46M/Iw7nkNRFLZHbGeY+zDan7iMX5SMnEnuPuKy48pqJcNSwnQ279Wsq2gUTXm92fbtop+lkRF5eXDffZCdLTqy2Nrq7LASiURyTyLFWRMg6XoSqbmpjG8/nrbWbdkTVbuwg9YMxP90pqg3mjuXoiKYMUM4tK9fL67VLQNdaAAAIABJREFUtfi5+QFwKOaQ2DBtmlBuv/xS7fx7IvdgbmJC96e/hB6/cH3Xyww8GY5n7gn69FWxZAksWCCebmBQq1O4p7A0shQRr1JhsytyFyevBZMxxIfWB4LoZN2BC6kXqk1zbdECunQpF2fau+RVImeld7Z//VWIpSefrP167c2EvWNZ5AyESszMFL7ZpQwfDrm5VWvi6ou47Dj0VfoMdR8KUG2d3q2ISIsgKiOKsa2HY/7oU/yxHlKSo3W8UomkfglPDS/7d1mrFB0QmS4cZD2sPcRn/cIFGDMGRREZEqdOiTR4mcYukUgkdUeKsyaANqWxm0M3RniM4EDMAYo1xXc8z+mE06BAh9W7wMuLy04DGThQNBxevBj69688vo9zH0zUJhyIOSA2tGwpboGuWFFtr60DMQcY2GYgRx47yLa1zswc9y7P8zXtOwsl9u238NFHMmJ2u1gZW6FRNGV3urUpebZTH4WEBAalmpKen861vMq9u3KLciksKcTHR9jpK4pwagTK+5xdv15mBqLRwI8/wqBB4OlZ+/Vam1ij1lNXjZxBpbqzIUOEW1uFFmj1Snx2PI7mjnS1F1eGtUlt1Lo03n9JjSotDfvr0GXTIZ2uUyKpb7Q3Jgz0DMoMm3SBVpy5W7uLqBnA2LF8/LEwAXn/fZHWKJFIJJK6I8VZA5NTmENAbEClbVpx1tWhK8Pdh5NVkMWp+PI6HoqKbsubPDAhkAfSHVEHh7Cy71f07KXi4kVYtw6eeabqeCO1Ef1a9ysXZyBSG69dg61bK41Nvp7M+dTzDHEdgkqlYkyH0fze4gKf2H3G5gPWnD4Nzz57+6+DRNScAWVpeGEpYbSxbIPJpAdAT4/ep0Va3YXUyqmNo1eMZv62+fj4iP9Vly9XiJxp0xrPnKFQo8+KrIn4+IgxtTUC0aKn0sPezL5y5MzTs4opiJWV8AnYsqVux7td4rLjcLZwxs3KDWO18R2LM0VR+OvSX7Rv2R6HP/6G1q0J7NSCMZtCIS+vnlYtkeie8NRwLI0s8Xb2JjRFd+IsKj0KAz0DWlm0EuKsXTu2hrfnjTdEB5XXX9fZoSQSieSeR4qzBuaLY1/Qf1l/ojOiy7YFJwXjqXLAdtIjjFsegHNWad1ZZqZw2LCzEw2ebyA4KZilgUsp0Qgb8dMJp3nhlJp3jT5gxs/D6N5dlI9NmVLzevxc/TibeLa8eHzECAqdHIhbvKjSuIMxB8V4Nz8hFA8dgr/+ErdL9fXr9qLco1gaCXGmrTs7l3KOLnZdwMYGBgygzQFR+1ex7ux64XWOXj3K+dTzlZpRayNn2rTGEz8F40Y0M7/sRU6OiJxVaENXaxzMHCqLM0ND0Z+tgikIiBrHs2dv2U9bJ8Rnx9OqRSv09fTpZNuJc6m3J84URWFHxA58l/my8/JO5tqOhH/+gTlz2Dy1By0zC4UTjkRylxB+LZxOtp3oat+VkKSQOjn/ViQyIxI3Kzf0Cwph717CfB5l2jQROF+2TGZLSCQSiS6R4qyBORBzAAWFDec2lG0LTgpmwVkL2L0b00++JGYxjHjuS3BzEw4bdnbCRfHMmUpzPbf9OZ746wn8V/pz6dolcmIi2HtsDu8UvM6cObBvH7Rpc/P1+Ln6oaBw+MphADR6Kn7sDa2OBJOysbwh9oHoAziVmNLnszWigc3gwVBSAo8/rqNX5t5DGznLzM+kRFNCeGo4XWy7iJ0TJ2IUep5XAvTp8N73MHo0PPYYsd99iFOGhqScJDw9hbnH8eOVI2fFmdeZt3ww+iaG7NgBYWFC2+viAsrBXDSiroS3tzCSqXAhOHmyeNy0qe7HvBVxWXE4mzsDt+/YWFRSxKgVoxizcgxJOUksnbCUVy87gqJw1ucJIp1GcNhdDR9/DPn59X0KEolOOJ9ynk62nfCy9+Ja3rUqn9Wsgiz+vvg3L+98mYmrJ962I2lkeqRIady/n5L8QmafeAZTU9i8GUxM6uNMJBKJ5N5FirMGpKikiOOxwiVh/fn1ZdsiEsK4b288jB0LEREcvr83raPSKB7QX1z0njwpcsUq5I7EZMRwMDABz8T/cvBIIT2+HE6XjQt4W1nErMlZ/PST6Gd2K3xcfDDSN2J/9H4A/gz/k1e6JXHOFgyffhaysgA4ErmfrZuM0f/2W+HjvmqVaESq9XSX3DEVI2fRGdHkF+fjaV9aFDZpEqhUfLKjBO/tZ0Rn502b6Pjv/xL7JTy7Pga1Wuii7dshKT0HAz0DTA1M+XHeSUJKPFn85jVGjxb1X7rCwcyhcs0ZiEXcYAri5ia6M9S3OMsryiM9P51WLVoB4GnnyZXMK2QXZIsBilLeDK4CAbEB7I7czX8G/4eLz11kXs/H0P/1Ny71nc7Ah13YvODfvNa7pfAIX7asfk9CItEBmfmZJOQk0Nm2M172XgCV6s6ObfqGNQOtmbt0PN+e+Ja/L/3Nh4c/vK25o9Kj8LDygG3bWGLwPIGXLPnqK2jVql5ORSKRSO5ppDhrQM4mneV60XV6OPYgIDaAq5lXuXjtIg8EF2ORkQsvvAAeHuR/uAiXl2DF+w9S3L2rEGYLF8KOHbB3L+npMO2JZPjuHGFL3qDwxwPk/vcKe6M+4mGn3fy8rsVtZxoaq43xcfERET1F4b2D79HGoT3fP+2NRUoWmgULuHY9lcd/C8U7NE2keW3aJHLkzM3r9wVr5lSMnGmjPV3sSiNn7dtDSAhPfj0K70/biZqu1FRe+3Q0h1vDhOBC8ovzWbBAaKL17z2ElaEd15JK+M/GHgyzPMX9r3fU+Zq1aY2KonDx2kW2XNiCxruX2HlDauP994tetYmJOl9GGdoeZ84W5ZEzKHWtu3pVRBydnOB8ZQfHPVF7UKHiRd8XMdQ3hEOHKLx8hUeSv0KthsI8I46cXUpe717w/ff1dwISiY7QOjVqI2dQQZxFR9N5zss8cUpDzBpHMsYdZm6Pufwv8H8kJl4W6byZmdXOm5mfybW8a3hYuZO45QRvKO8zcmTt+yVKJBKJ5OZIcdaAHLlyBIAvRn0BwMbzGwlOPMsLAZDfoa1oEAUMajMIBzMH5v45F5tPbBi/ajxHJ/SE1q1ZMW8/HTooHF3fi7Z9NhP01ia2TvqJT92+4hvms/zHojsuARviOoSgxCBWBK/gTOIZFg5aiN/UBXzpC3pLlpAz4yGePgVXn54Oc+fq9DW5l6kYOdOKs862ncsHeHpi3bknERmRwr1TT4/1hhFs6qLCIwNSLwUzbpzQDldPdaVw81e8OTOGLI05X7+XWS91IA7mDhSWFNLqi1Z0/LYjk9ZMYqs6UjSkrWAKAiK1UVHgzz91vw4tcdmix1krC3ELv4tdF1CgcOkS8PIS6lBRqkS/9kTtwdvZu7z1wM8/s9DgUwKjbfj5Z5j7UgRcnMh35k/BpUsihVciacJoxVlnu87Ym9lja2orxFlWFiXjxqIUFrJ8wRhMisHYbxj/jevEB9sLadHeE/z9xedl584q80ZlRAHQNdOIl648TwFGfPedrDOTSCSS+kKKswbkyNUjuFq6MtR9KN0curH+/Hqyd/1Nz0RQ//ulsl87M0MzQp8JZc0Da3jE6xFOJ5zmvs3Tea7jn8yMeo+2nGeljTcRJx6kx3v3M37n87zc8nfmLzBHPW70Ha/Lz80PjaLh2W3P4mHtwfRu05nUaRJfj7Ml3tEM18372NJZD/vF0hxBl1SMnIWlhNHKolXZNi0dbTpSpCkiOiOatLw0LqdfJqu3sIzPOyj64T35JLhNWk5mwBR+3N2W+VYr8Jw/tF7WPLDNQDradGSw62B+GPcDJmoT9sYegm7dqkTOPD2hXbv6TW3URs60aY0e1h68eUSfAe/+DD17QnAwTJgAy5cL11PKHVOHuw8XkwQF8c/KVD4r+hdPPy1E5dPzC8BtH28fnUlUUSuR3iiRNGHCU8Mx0DPA3codAC97L84nhsDUqaguXuSBh6Dts28KByEPDxyeeYXnj8M2tyIyfvoWzM25MPo5nvPax3NPFfH11yJl+u9dWRAxklPftWYV03ntmWzat2/kk5VIJJJmzG1UJUl0gaII0w1to9wpnafw9v63eXdTCOlm+ljPmlNpvK2pLVO9pjLVayqzOs1n4NgrfHupJ8/ZrOSL1NmcbqXh+gfvYvbAw9C2bZ0cE31dfDHQMyC7MJvF/otR64m3xUN95nDffV/ybJgFa2f0ZKKhrPzWJSZqE9R66rLIWVlKYwU62orUxAupF8r+v3QYMZXcj4PRP3oMSptKW49ZjFWsKVmBPXjnLUW3hWYV6NuqL+Hzyxvdrj+3nv0x+6H3AFi5Ujh5lh5bpRJCZ/FiyMgQ2bm6Ji5LRM60aY3qlGu8fkjhWG8H+u3dK9by6KNCIW7bBpMmcTDmIMWaYkZ4jIDsbCInv8RMzR94dirm88/Fa9zK0gnumwhLwnmSH9kZHS2McCSSJsr51PO0a9kOA33Rd7KHVWd8314KZ4tZ8kRPLnRMwtfFF1R6cPgwrFvHlT7tmLpxCLNK8sjvGsIfF/QwDCvA4EIR2cUGpTMPBnbyH6CT4WVe+7htY52iRCKR3BPIyFkDEZ0RTUJOAgNaDwBgSpcpuKcpDDmbycExnWu0vCopgRene8Flfxj3FP5bNfRbaMN/P5uA2etvQYcOdbayNzUwZUCbAbhZuTGz28yy7fN6zeOkYwlzhmfj02FYnY4hqYpKpcLSyJKM/AzOp56vXpzZlIqzaxc4GX8SAP/OEzjRCixOBZeNy8hP53vjl7hk7YPVUw83zAkg3D5DkkLI6dpJmMdUMAUBIc6KikQj9PogPjseUwPTshRRFi3CqEjhzdEG5QLV3x8cHYXjKbAncg9G+kYMcOlP+qMvMS7mO4rNLNmwWV32MbQ1tUXdMp7BY3eyi1FcPZVUzdElkqZDeGo4ne1K06JTUnj17Z1MPVtM0hv/4qU253mg8wPoqUo/E+bmMHcuWcWDcNl+jF+ffJk//1bx75ch5uVvySw2I3H1Pg4dggnvf80c36GcpienfjuHsXHjnaNEIpHcC0hx1kBoreoHthkIiLqAz05YUagPcTMm1fi81atFFspPyzR0HXeUKXuf5JRBMjO6ztDp+lbdv4pDcw+V3XUFEbUZ7DoYgCFuQ3R6PInA0tiS4KRgcotyqxVnNqY22JjYcCFViLOONh1pb9Oew23A5sJVyMkBoCArjV6n4tCb+mCDelv7uYlWDCcdS2uybkht9PERfhxr1tTP8bUNqFUqlRCGP/5IyEQf9hrEciC6tLm6Wg2zZgmFmJjInqg99G/dH/3f1nH/+ke4rN+BTVsN6FjBP0VPpYejuSMWQ8TndsNOi/o5AYlEBxSVFHE5/TKdbDpBeDj4+GAffpUHH4RX+6STX5zPlC7lDS+Tk4UhbM+ekBbmjXrIR+TNt+cnGxue7naAwrZuOCycx0DvPFqabeSHwAP0nNgGs4cnNOJZSiQSyb2BFGcNxJGrR2hh1AJPu1Kr9IsXmXQik+/7QFvPQdU+p7AQ/vMf8QM6Z5aaXyb9QmFJIS2MWjC+w3idrs/JwgmXFi5Vti8ctJCBbQbi4+Kj0+NJBJZGlpyKF4KmOnEGQiRfTLvIybiT9GnVB2O1MUFtTdAr0cCJE2gUDX5nMzEqKNZNp+k7oG+rvhjpG/G3YbQQhSdOVNqvpwfz5ol+5TcYJuqE+Oz4MjMQ3nwTDA3psHgFHtYePLblMXKLcsW+uXOhpITsn5cQnHCWV0+b8/hT+uxnKL/8osLPr+rcTuZOZFmH0F0dxrrTHrpfvERSyl8X/2L+tvm1bhodkRZBsaaYTradYPZsyMkhd9c21nvCiuAVOJg5lGVt5OXBxInC++O99+DqFX2ubp3DyhnfMKXzFHbHHeKtqfaig/yiRcxZegpFTw+++UaXpyyRSCSSGpDirIE4cvUI/Vz6oa9XmoL4zjtgbEzi/NkMcq1enP3vfxAdDR9+KC5yvZ29WTJ+CV+M+gITg4aJjoxqO4pDcw9hrJa5LPWBlbEVBSUFwA1OjRXoYNOBE3EnSMhJoI9zHwCiOzmhUQGHD5OZn8nDIZBtbwUDBzbU0gHRisHXxZd9cYehd284dqzKmOeeE7rt0091f/y47DhhBnL6tAjPvfgipq5tWTZxGZfTL/Pm3jfFwE6doH9/+PFH/vlNnzVf38/vmhm8t+A602dW/zXoZOFEfHY8U5yPcjS5PbGxul+/RAKwKmQV3538jsCEwFsPrgatU2PPawbiBskbb2AxaDguLVzQKBoe6PwA+nr6aDQwc6YYsmqVuPlnZQWO5o5M6zqNpROX8nzf5/nU8CQ5j0xB+egjhpy7zu7Zg6BNG12eskQikUhqQIqzBiA9L53Q5NCylEZCQ2HNGvSe/xefTP8VUwPTKs/JyYFFi2DIEBg1qnz7vF7zeKzXYw2zcEm9o3VndDBzwMbUptoxHW06lkWAtOLM1N6ZqFZmcOQImQlR+EdAzGifejMCuRl+rn6cSTxDQe+eEBQEBQWV9tvZiejZihWi9ZiuUBSF+Ox4nM2d4ZNPoGVLeOUVQKThPt37aRYHLObY1VLB+OijGF5J45ura/mNObz7jsKbH5rVOL+TuRMJOQk82P0iABs36m7tEklFItMjAVgaWDtH3POpIizdYcsRMDCA6dMByvqdaVMaX3sNNmyAzz4T9aDVof19+X6qOxprK4IcIX7ulOoHSyQSiUTnSHHWAByLFReH2rQS3n4bLCzKLiSrY/FiURfw4Yeyn0xzRmtkUVNKI5Sbgqj11PRw7AEIMXfC3QCOHUO1bj2GGkidNLL+F1wN2lYMoR7mIhc3KKjKmJdeEkaOX36pu+Om54taGhdTR9FEd/JksCxvRfDxiI9pbdmaWZtnsTJ4JZeGD8fLbjtbNQ+weDG89bbqpp8tJ3MnUnNTadfVgK4Es+4Pje4WL5FUQCvOVoWuIqcw546fH54ajrtpKwxXrRU5i3Z2gLhx0ta6LX0cBvHyyyJ6/cwz8OKLNc/lZuXGyLYj+TZyDcf//hG/OeBm2642pyWRSCSSWiDFWQNw+Mph9FX69G3VV6RfbdwI//63uNNfDdevix/R++4DX98GXqykQbktcVZqp+9l71WWzupg5sA+50LIzsZh8U9cbAn63n3qf8HVoG3FsN02XWwICKgyxtUVpk0TqbrXrunmuFobfa/oXOHV7+9fab+FkQW/3fcbaXlpzPj9ZTr0v0ZEqh+PLNzNv/516/m19vyZTi2ZwnqOHFXJdmcSnZNTmENKbgrjO4wnpzCHtaFr73iO0ORQZsXaQEoKBTPnkZwsboYsGLCANQPD6eej5vPP4amn4Kuvbn3D7/Fej3M16ypfx24g21j0D5RIJBJJwyDFWQMQEBtAD8cemBmawdq1Iu3khRdqHL9li3Alv8kQSTNBm9Z4M3HW1rotaj01fZ37lm1zMHfgHyeR6micmMLqrmBtWr3Yr29MDUzp26ovW3MCRV1KNeIM4NVXxY2H777TzXG1Dag7nLgs2kmMGFFlzBC3Iewbk4zjmmgMM7oyYuHXfPV699ua38nCCYAkOxMeZB2KopKpjRKdE5UeBcCMrjPoYteFpafvLLXxeOxxghKDmBFYhOLkzPhvRuHgIOo827ZV0d9XzbVros3fDz8I89JbMbHjROxM7VgbthYVKlytXGtzahKJRCKpBVKc1TMlmhJOxJ2gn0s/seHUKejevVL61Y2sXg0uLjCoep8QSTNCGzkrc/GsBiO1EVse3sKbg98s2+Zg5sAVSyh2dgRgtRdYG1vX72Jvgp+rH4HxgRT17V2jOPPygnHj4Pvvobi47seMyxaRM/vDp0WIuZou1//8A4MG6qPSGHHssCG7Fr2InZndbc3vZC7E2VUbNZ0Jx9M5nXXr6r5uiaQi2pRGD2sPHu/1OMfjjhOSFHLbz3/v4Ht0KbKmbcBF/h7wAbv36PHYYyJ1sX9/ePJJCAmBMWNuf02G+obM6TEHAJcWLhjqG97JKUkkEomkDkhxVs+EJodyveg6vi6+Is8kMFC42tVAWhrs2AEPP9wo3g6SBsbT3pOWJi3p5tDtpuPGtB9Da8vWZX87mDuACtKHD+BKd3cu2IG1SSOKMzc/SpQSLnewhZgYSEiodtzcuZCUBPv31/2Y8dnx2OWAUVBIlZRGRYGPPhIXpG5uQi/26nVn82sjZ1FmRaCnx5S2QRw6VOOpSSS1IipDRM48rD2Y2W0mhvqGtx09OxV/im2XtvHNNR9KSuCV0w/ToYOIkH30kTDh+eYbsKnea+imzOs1r2xdEolEImk45OV/PRMQK6IIvi6+EBEBmZk3FWcbNkBRUYO3q5I0Ev7t/El9JfWOhZWDmQMAJ9+cy/cfT8FQ3xATdcM1n74RrYtkoGtpE/Pjx6sdN3as8MJZvbrux4zLiuP+WHPxR4WwQE4OPPQQvP46TJ0KR4/WzgXc3swePZUecfnJ0Lo1D7b4B0WBTZvqvnaJREtkeiQWhha0NGmJjakND3R+gOXBy8kvzhch5hMnxN2Galh0cBEuKiuGbDvHsrYfEh5pxMcfi8z5utLBpgPP9H6GhzwfqvtkEolEIrltpDirZwLiArA1tRV3H0+JZsP0qdm4YdUq6NhRNJ6W3BuoamHH6WAuxFnS9WTSCjOwNrau1Ty6wsrYClMDU4Kd9MWVYQ2pjSYmcP/94ibEDY77d0x8TjzjI9XCma70A1NQIMzqNm4UduGrVoFZzW75N0Wtp8bezF7Utrm745l+mM6dkamNEp0SmR6Jh7VH2ed3dvfZZORncHTnMhgwAHx8YN48YtOicfjMgQf+eICQpBCCEoLYcmELf55sx/Ur6byd/i8GDYJJk3S3tu/GfcczfZ7R3YQSiUQiuSVSnNUzAbEB+Lr4ih/eU6fA2Bi6VG/+EBcHBw6IqJm0z5fcDG3kLOl6Eun56Y2a0ghCYDpbOHOlMFkIpRrEGYj3d2YmbN9e++MVa4oJjj/DwPBcGD0a9PTQaGDOHNi3D377Tdj31/VzpO11hpsbREczZQocPChSMyUSXRCVEVUpdXBYGz/ePGHCwPueF9kWM2bAzz9z/f7xZGQms+vyLrot6ca4VeOYGmlKr79O8ZHvZpLSDPnsM/nbIZFIJHc7txRnKpXqZ5VKlaxSqUIrbGupUql2qVSqS6WPjXtl2ERJz0snPDW8shlIz5412mWtXSuyV2RKo+RWmBmaYWZgRlJOEul56bQ0aRynxoo4WziLKJOvL5w8WaPrx/DhIti1alXtj7U6ZDU24Vewyiosqzd79VVYswY+/lhcz+oCJwsnErITwN0d4uN5cGIBGo1sSC3RDYqiEJkeibuVe9k2g9cXsmhbHrva61Fw9jQsX47y+ed0PBDG4Y2WXB2xjTcHvIFxWhY/bDHiX7Yr+SBgGDNmQN++NzmYRCKRSO4Kbidy9ivgf8O214A9iqK0B/aU/i25gRNxJ4DSerOSEtHj7Cb1ZqtXg7c3dOjQUCuU3M04mDuUR84a0alRi5O5kxBn/fpBbq6wiKsGtVrUhG3dCtnZt55XuaHepkRTwvuH3mdOoiOKSgWjRvHDD/D55/Dcczft7X7H2JvZk5KbIiJngJdFDB07wvr1ujuG5N4lMSeR/OL88sjZX3/BF18QM20c4x8sZk9eGADB04YzZxJ4X8jGsu8gFj3yP/au6o1/5g6+Tp3GCy/AsmWNeCISiUQi0Rm3FGeKohwE0m7YPAn4rfTfvwH36XhdzYKA2ABUqIRZQni4aPJUQ73Z5csisCajZpLbxcGsVJzlNX5aI5RHzhQfH7HhJqmN06ZBfj5s3nzzOdPz0nH/yp239r1VJtLWhq0lKukiM88boPL2Jr7IjldeEQG0L7/UbVqXpZElmfmZInIGqKKjePBB4TaZnKy740juTSra6BMbC7NnQ48eOP64EktjS9afE3cBVgSvYKW3mszzZ1B++ZVfO35Izyt/Em7UnfXrxfveULrdSyQSSbOgtjVnDoqiaA2lEwEHHa2nWREQF4CXvRcWRhblZiA1RM62bROPkyc30OIkdz0O5g4kX09uMpEzZwtncotyyXK0BmdnUfxVA/36gavrrV0bt13aRkxmDIsOLuLt/W+LqNnB9/km0A7LiKvwxhssWCAyKL/7TvSi1iWWRpZkF2ZT0qa0jUFUFFOmiK4Y0rVRUle0NvruFq3FnbnCQli7FiNzSyZ2nMjm8M0UFBewKnQVY9qNIcuwK/6rZzP3yDy8+lsSGGLEAw808klIJBKJRKdUX/x0ByiKoqhUqup9fgGVSvUE8ASAg4MD+3XR4EjH5OTk6HxdGkXDkegjDLYbzP79+2n35584mphwOD6+WjeBlSu74uJiwpUrJ7hyRadLkTRBdPGeK8ks4UraFbKKs8hMymz0z1ZGUgYAf+7bwoiePbHfto0ju3ej1FBjOWiQOytXtmHRojAGDUqtdsxP537C2sCafjb9WHRwEdtDtmMZfJ7Hd6hI8PdnfawrK1bAjBkxXLkSpfPPTkpsCgDbw88yVq0m9uBB0jp2wsWlL0uX5tOxY7BuD1iP1Mf3nKRu7IneA4De21/B4cOce+MNkuPjIT6ejiUdWZ6/nLm/zyU+Ox7fmMV0fqwYRVHx/POXmTQpnthYEXBrysj3naShke85SUOj8/ecoii3/A9wA0Ir/H0BcCr9txNw4Xbm8fb2Vpoi+/bt0/mc4SnhCu+gLDu9TGzw9VWUwYOrHZubqyjGxory/PM6X4akiaKL99xbe99SeAeFd1C+PPZl3RdVR/ZF7VN4B2VP5B5F2bxZUUBR9u6tMk6j0SiKoijXrytK376KYmKiKMePV52voLhAsfjAQpn35zylRFOizNk8RzF7HSXG1kDRuLkpxWmZSs+eiuLioig5OfVzTj8F/qTwDkp0erSitGunKA89pCiKorzxhqLo6ytKSkr9HLc+qI9bMKTtAAAgAElEQVTvOUndmL1ptuLyqbOiuLoqyvDhlfblFeUpFh9YKLxppBj4LFVAUQYMUJSoqEZZaq2R7ztJQyPfc5KGpjbvOeCUUoNeqm1a4xZgdum/ZwN/1kEfNkuOxR4DSs1AiorgzJkaUxoPHBD1NxX66Eokt0Tb6wxoMmmNgDAFGT4cjIyEwUEF5m+bj+8yXzSKBlNT2LIFHBxgwgSIjq4838GYg2QXZjOx40T0VHr8NHQxxwN70vpaMarff2fp2hYEBYl+ZrXtZXYrLI0tAcgsyCyz0wfRq62kpMrpSSR3RFRGFA8ktYSYGJg3r9I+Y7UxI50egWWHKTo+j5dfFpnCpd40EolEImmm3I6V/mrgGNBRpVLFqlSqx4CPgJEqleoSMKL0b0kFAmIDsDSypJNtJwgLE+qrBjOQ7dtF+zM/vwZepOSuRtvrDGgSVvpO5k5AqTgzN4chQyqpl4LiApYHL+dE3Ak2nhde9A4Oot6ysBDGjoWsrPL5tlzYgonahOGO/eDzz9Fv1x7PnUEob77FJ8cGMX++OMRDD9XfOVkZWwGUm4JEiRqhXr3AxQX+lLelJHUgMj2Sh05cBysruK+qr1bujjcgqTvv/xjKp5+K/u4SiUQiad7cjlvjI4qiOCmKYqAoiouiKMsURbmmKMpwRVHaK4oyQlGUG90c73kCEwLp7dwbPZXeLc1Atm+HoUPBxKQBFyi566kUOWsCbo0WRhZYGFoIcQYwfjxcvAiXLgGwO3I3WQVZmBqYsujgIjSKBoDOnWHDBjH0scdErz9FUdhyYQuzTHwx9ewBL78MPXqQtuMEk4LeYcECEb3688/6bbpraVQhcubqCikpkJuLSgWTJsE//4iuARLJnVJQXEBOUix9Aq4K+1Jj40r7Q0Nh5zpXZs/LZeETXo20SolEIpE0NLVNa5TcBI2iISw5jG4O3cSGwECwtIS2bauMvXxZXLvKlEbJnVIxctYU0hqhQiNqgHHjxOPffwOw/vx6rIyt+Nr/a4KTgtlyYUvZ84aZHef9QdtYvx6+/hpCkkMovBrDp58FQ0EBHDjAlZ920vfZPvzzD3zzjWja3qJF/Z5PWVpjfia0aiU2Jgij2vvug7w82L27ftcgaT4Ua4rLWkLEZMYwNRQMCovh0UcrjVMUeOkl8bPxxYeWjbFUiUQikTQSUpzVA1HpUeQV5+FlX3q3MzgYunev9hb/jh3i0f/GNt8SyS1oapEzuEGcubuDpyf89ReFJYVsDt/MpI6TmN1jNu1atuO9A++hJCZSPGcW+PqyYP84xqv/4uWXNPxv+TF2rACz7HzYvp1Yj8EMGwapqaJGc/78+o2YaakUOdOKs7g4QKQhW1rK1EbJ7ZGel063H7rhv9Kf3KJcItMjeTQIrnduK/JkK7BjB+zcCW+9BS0bP2NZIpFIJA2IFGf1QGhyKACedp7iFmhICHTtWu3Y7dtFQK19+4ZcoaQ5YGFogbFapEI1ycgZiOjZgQMcCN5KRn4GU7pMQa2nZuGghdgfCqKwvTvKihV8PABmLGjHi7YzaF0SzcZXx3EyZQ5Xl+wg3rk3w4aJjMJ//hE90hoKbeQsIz9D9G4DiBfnZ2Ag6uS2bhXmIBJJTWgUDdM3TudS2iV2Xd7FpDWTSA7YQ994KJ41s9KdhuJiETVr1w6eeaYRFy2RSCSSRkGKs3pAK8662HURLlzZ2dWKs/x82LtXpjRKaodKpcLBzAEjfSNMDJpGwaKTuRPx2fFlqVuMHw/FxVxa+z0WhhaM9BgJwPQ24/l9qz6XTPIZ8IIFXj//xfIPL3By9SsM6zMFjaLHPOUX3GYMpG1bkUm4Ywf4+DTs+RirjTHUN6yc1lgaOQOR2piSAseONey6JHcX7+5/l+0R2/lx0CfscHgJr5W78Xjzcwr1weLRpyqN/f13OH8ePv0UDA0bacESiUQiaTTq3IRaUpXQlFDcrNywMLKAkP1iYzXi7MABUbMixZmktjiYO1BQUtDYyyjD2cKZgpICMvIzRKplv34o1ta033yISYumYKQ2AsDgP29jl6PwyWtDWPf0r7hauQKwYMhCDvwxkIRDT/BC2584f8qRs2fh8cfB17dxzsnSyFKkNVpaCteeCuLM319E0DZvhoEDG2d9kqbN1gtbee/ge8ztMZe585ehCgtjFJBiqvDdGFtetHeoNP7HH0U28KRJjbNeiUQikTQuUpzVA6HJoeX1ZiEh4tGrqtvW+vXCcXzo0AZcnKRZ0bpFawqKm5Y4A2Gnb21iDWo1l556kJEf/o+OqxLhAY1wL/3hB1TPPcdnr39VZQ4/Nz/83ERfiZH9G3T51WJpXCrOVCoRPYsvT9ts0UK0dPvzTxHpaIg6OEnTZ0/kHrZHbGdf9D6CEoLwdvLm+y6vogrrDK++Ci+9xIHUg7S+4XkhIXDiBHz5pXwvSSQSyb2KFGc6prCkkPDUcMa3Hy82BAeLrqE32MoVFcGmTTBxorTQl9Sez0Z9Rk5hTmMvo4yK4szT3hOAL/qraD3MgIWb94kQWGCgqN9atKgxl3rbWBpZirRGEOKsQuQMRITj6adFKlqXLo2wQEmTIjI9khHLR2Cob0g/l3687fc2T/d5GuM1W8WAWbPA3p4p9lOqPHfZMpHKOGNGAy9aIpFIJE0GKc50zKVrlyjWFFeOnFWT0rh/P1y7Bg8+2LDrkzQv3KzcGnsJlagozkD0K/vr4l/0e2oSDOhcLsg2bKh/H3wdURY5AyEqjx+vtF/rtLp/vxRnEojJiAFg+/TtDHMfVr5jzx5wdKzxTVJQAMuXizpGW9uGWKlEIpFImiJSnOkYrRmIl72X+LW9cEH82t7AunUipXH06IZeoURSfzhZOAHl4uzCtQvEZccxymMUTJkHFhaQnAyTJzfmMu8ISyNLknKSxB/ayJmilOWdubqKa+7jx6W7nqT8ve/SwqV8o6IIcTZyZI35ips3Q1oazJvXEKuUSCQSSVNFijMdE5YShr5Kn462HSEsXHhs3xA5Ky6GjRthwgSZ0ihpXpgamGJlbFV2gboncg8Awz2Gi4vSV15pzOXViiqRs4ICSE8va0ClUgkXyYCARlykpMmgfe87mTuVbwwNFTclhg+v8Xk//SSE/k2GSCQSieQeQFrp65jQ5FDa27QX/aeCg8XGbt0qjdm3T6Y0SpovzhbOxOeIC9TdUbtxs3LDw9qjkVdVe6rUnEGVujNfX7h4UUQ+JPc28dnxWBhaCLdeLXvETYqalFdUFOzeDY8+CnryV1kikUjuaf7f3n2HR1Xlfxx/3ySkkkJJCAkt9BIEpAgiCKKAva66thWRFX8WLGtfV9QVXXXtXdRVEV3UVVAQMPTeWyAkdAkJEBIgvc79/XFSSUJJJpkkfF7Pk2fCnTszJ/E6ySffc75HPwacrFynRk/PcjtMF01pLFqrItKQFG1EXeAoYMGeBYyIqN+lgCDvINJy0yhwFJTbiLpI0f5rq1fX8uCkzklITyie3lssKsr8HGjTpsLHfPWVqcCOGVMLAxQRkTpN4cyJsvKy2Jmyk8jgUuGsWzezEVKh/HzTpVFTGqWhKgpn6xLXcTznOBe3v9jVQ6qWQK9AANJy0yqtnPXrZyoemtooCWkJxY1xANOad9Gik85XnDPHBPzWJ/bWFxGRs47CmRPFHInBxj5pp8aFC+HIEU1plIYrrHEYiWmJRO2OAijbsa4eCvQ24ex49vFKK2f+/mYrQ4UzKRfO1qyB9PRKw1lamjnlovr9v4mIiDiJwpkTFXVq7BHSwyw+OXCgXDibMcNUzDSlURqqlv4tyXPkMW3rNHqG9CTEL8TVQ6qWosrZsexj4OUFzZqVq5yBqXysWgUOR22PUOoK27ZNOGtcKpxFRZk5i8OHV/iYpUvNjIpK7hYRkbOMwpkTRR+OxtPdk45NO5qqGZRrBjJnDgwbpimN0nAVVQ02HdpU76c0QqnKWU7lG1GDaQpy7Bjs2FGbo5O65Fj2MbLzs8tWzubNgz59TKivwIIFZub7+efX0iBFRKROUzhzoujD0XRr3g0PN4+ScFaqcrZnj+nopqqZNGSlfzGt781AoKRyVtyxMSys3LRGMOEMNLXxbFbURr/4/4HMTFix4qTrzebPh0GDwNe3NkYoIiJ1ncKZE21N2mqmNIIJZ02alKxRwVTNQBtPS8NW9Iuph5sHQ9sOdfFoqu90K2ddu0JAgMLZ2axcOFu92jQEufDCCs8/ehQ2bNCURhERKaFw5iQZuRn8cfwPujXvZg5s2GCmNFpW8TmzZ0O7dtC5s2vGKFIbijbfHRA+oOxeT/VUhZWzQ4fMQqFS3NxgwACz7kzOTuXC2ZIl5mdAJXMWFy82axTVDERERIoonDnJjhSz0KRLsy5mKsuGDWauSqG8PDN9ZdSoMnlNpMHx8vBiVIdRjOndMDZtqrByZttw8GC5c887z+w9n5FRmyOUuqIonBXvc7Z0qWnj2aRJhecvWADe3iX75ImIiHi4egANReyRWAC6NO8Ca9eav6qX+mvpihWmZbLWm8nZYPZts109BKfx9vDG092zpHJWtNdZQgK0alXm3IEDoaAA1q2DofV/RqecocT0RAK9AvFt5Gt+BixfDrffXun58+fD4MGmCaiIiAiocuY0sckmnHVs2tH8QIYylbPZs8HDQ9NXROqjQK/AkspZ0TrSStrpg6Y2nq3K7HG2ebPZ32zIkArPTUoyS5P1M0FEREpTOHOSuOQ42gS2MX8xXb4cunSB5s2L758zx2S1gAAXDlJEqiTQO7DstEaosGNjcLAppm3aVIuDkzqjTDhbssTcVhLOFi0yt2oGIiIipSmcOUlscqxZb2bbJpyVmtJ46BCsX68pjSL1VaBXYMm0xuBgUwavoHIG0KsXbNxYi4OTOqNcOGvbttzU1yLz50PjxtCvXy0OUERE6jyFMyewbZvYI4XhLC4OkpPLhLPffze3aqEvUj8FeQeVVM7c3KBly0rDWe/esH07ZGfX4gDF5WzbLglntm2agVRSNbNtM5viwgvNBtQiIiJFFM6c4GD6QdJy00wzkKL1ZoMHF98/c6b5Y3ufPi4aoIhUS6B3qcoZVLoRNZhwVlAAW7fW0uCkTkjOSibPkWfC2c6dZspEJeEsLg5274bLLqvlQYqISJ2ncOYERc1AujQrDGdNmpg1Z0BWFvz6K1xzjfmDu4jUP2UagkClG1GDCWegqY1nmzJ7nC1dag5ecEGF586aZW4VzkRE5ESKC05Qpo3+smWm80dhEps71zTs+tOfXDlCEamOQK9AjmUfKzkQHl5p5ax9e7OWSOHs7FImnC1ZAs2aQbduFZ47cyZ07w7t2tXiAEVEpF5QOHOC2ORYfDx8aJXvCzExZaY0/vADNG0Kw4a5bnwiUj2B3oGk56ZT4CgwB8LC4PjxCnebdnODc85RODvbFG9A3bilqZwNHgyWVe68tDRYvBguv7y2RygiIvWBwpkTxCXH0alZJ9xWrTYHCpuB5OTAjBlmSqMWfYvUX4FegQCk5qSaA0Xt9OPjKzy/d2/TTt/hqI3RSV1QHM6y3GHHjkrXm0VFQV6epjSKiEjFFM6coLiN/vLl4O4O/fsD5odwairccIOLBygi1RLobcJZ8bqzjh3N7c6dFZ7fu7epkOzdWwuDkzohIS2Bpj5N8Y7ZYQ6ce26F582aZfa7LDXBQkREpJjCWTXlFuSy5+geE86WLTO/lfn5AfD99xAYCCNGuHiQIlItRZWz4o6NnTub27i4Cs/v1cvcamrj2aO4jf727eZA167lzrFtE85GjtRsChERqZjCWTXtStlFgV1AD582sGIFDB0KQG4uTJ8OV18Nnp4uHqSIVEu5ylmzZmYxaSXhLDLSrD1TODt7JKYnmnAWGwv+/mYvvBNs2mT6yGhKo4iIVEbhrJqK2uj325RkFpldcw0A8+fDsWOa0ijSEJSrnIGpnlUSznx9zW4aCmdnjzKVs65dK2wGUtRC/9JLa3lwIiJSbyicVVNRG/0289aZnaYLFxJMm2b+eHrJJa4cnYg4Q7nKGZw0nEFJUxBp+By2g8S0RMIaF4azwn0uTzRzJvTtC6GhtTxAERGpNxTOqik2OZY2niF4zp5rqmbu7iQmwtSpcOON4O3t6hGKSHVVWDnr1Ml0a8zMrPAxvXrBH39ASkptjFBcKSkjiQK7gLbuzWD//grXmx04YGa+X3WVCwYoIiL1hsJZNcUmx3Lz4WCz0/R11wHw2muQnw9PPeXiwYmIU1RaOYOTdmwEVc/OBkVt9DsmF+6dUEE4++EH0xDkxhtrc2QiIlLfKJxVg23bxB6J5croPNMb+aKLOHQIPvoIbr0VOnRw9QhFxBm8Pbzxcvcqv+YMKp3aWBTOtO6s4duWtA2AtolZ5kAF4WzaNLM5eQV3iYiIFFM4q6Jj2ce4ftr1HEtPpt/aBLjySvD05PXXTV+QZ55x9QhFxJkCvQPLVs6K9jqrJJy1aGE+Nm+uhcGJS/1n039oE9iGiEM5pk1n0bVRaP9+sw2mqmYiInIqCmdVsD5xPX0/6csvcb/wbci9eB8zUxqTkuCDD+DPfy75o7qINAyBXoEcyz5WcqBxYwgPP2lTkB49YOvWWhicuMzuo7uJ2h3F2D5jcYvbAe3bg5dXmXN++MHcKpyJiMipKJydoZikGAZ/Ppic/BwW/mUhf4p1Bx8fGDWKN96ArCxVzUQaonKVMzhlx8YePWDbNnA4anhwVZSTn0PkB5GMmzGOrLysKj9PYloiE36bQPBrwaxLWOfEEdZ9n63/DDfLjbv63FVpp8Zp06BPH9NDRkRE5GQUzs7Qi4tfxN1yZ/W41Qxu0Q9++glGjybHw4/Jk81fRrt1c/UoRcTZAr0Cy645g9MKZxkZpmtjXbTr6C62Jm1l8obJDPxsIDuSd5zR4w+lH+KROY/Q/p32fLDqXfpuOsKKPYtraLR1T74jny82fsGlHS+llV9Lcy2csKhs3z5YuVJVMxEROT0KZ5g9ak7H9iPb+S76O+7rf5/ZbPThh01/5HHj8PIyC/9ffbWGBysiLlFp5Sw52XxUoEcPc1tXpzYWhbEXh7/IgdQD9P2kL/P3zD/l45Iyknj898eJeDuCt1e9zc2RN3OQx5j9DURM/rGmh11nzIybSWJ6IuPOHWcSeHZ2uXCmKY0iInImzvpwdsdPd/Ba7Gunde6kJZPw9vDm0fMfhS++gA8/hCeegEsvBczykzZtanK0IuIqlVbOAHZUXHHq3t3cbttWgwOrhrhkU/W7f8D9bLhnA019mvLSkpdO+pgFexYQ8XYE/17xb67vfj0x98XwRbuHaDbpTQosOG/GOsjLq43hu9zkDZMJbRzKZZ0uM1MaoVw4mzYN+vUzS9FERERO5awPZ409GzM/aT5Hs46e9LydKTv5Zss33NvvXkK274d774WLL4Z//rOWRioirhToVUnlDCoNZ02bQmhoHa6cpewg2DeYIO8gWge25sYeN7Jk3xLSc9Mrfcx7a97D38uf6Huj+frar+ns18bsHdK8Oa+N7U/zlGz4seFXz+JT45m1YxZjeo+hkXujCsNZUhKsXg3XXuuiQYqISL1z1oezceeOI9eRy5TNU0563qQlk/B09+SJzmPMZtMtWsC334KHRy2NVERcKcQvhPTcdHal7Co5GBEB7u71tmPjjpQddGpW0qVidMfR5DnyWLBrHrz+OnzySZnzs/OzmbNzDtd0uYZuwYWLa595BrZu5fOb5vDMFyt4wvcxePNNs+NyA/a/mP/hsB2mEQiYcNasGTRvXnzOqlXm9oILXDBAERGpl876cNanZR86N+7Mp+s/xa7kl4ntR7bz1aavuK/nWEJuuwcOH4b//a/MD2ERadju7H0nfo38eCLqiZKDjRqZgFZPOzbGJcfRuVnJvh+DWw+mue1Lq7sfgcceg/HjYcmS4vsX7l1IRl4GV3W5yhyYOxfeeIOPL5zK2Ld64u2Xy6uZrzJldSfTBaMBi0mKoYl3Ezo06WAOxMaW69S4YoXJ7v36uWCAIiJSL5314Qzg8paXs+XwFlYfWF1yMDUVUlPZfXQ3l3x9CU28g3hhSoLZSfTrr6FvX9cNWERqXUv/ljx5wZP8GPMji/eV6kh4Gh0bMzNN1766JD03nYS0BDo1LamceSWlsPzrRvRasRv7pZfMQqk77jDvh8CM2Bn4NfJjeMRw2LsX/vxn3m05ifGL/szll8NHv8/Fre18xvAFvz85z0VfWe0oqjpalmUObN9ebr3ZypXQqxf4+rpggCIiUi8pnAEjQkbg28iXT9d/WnJw8mTskGDiLoxk1JqjRCdeh+/3P8FLL8ENN7husCLiMo8MeoRWAa14ZM4jJV1ei8JZJZX3utqxcWfKToAylTNGj6ZdYhZX3ww77/kTfPWV6UL40EPYts0vcb8wssNIvPNsuP56fsq+lAcTn+Kaa8xkgh6tW+P487W0b3KA6xZPYMvcRBd9dTWvTNXx6FE4dKhMOCsoMOvNBg1y0QBFRKReUjgD/Dz8uLnHzXwX/R1pOWkA7Ordlv+c50WvvdlM/jaDFm99CrffDk895eLRioir+Dby5eURL7MucR3fbP7GHOzc2ZTGEhIqfExROKtrHRuL2ugXV85274bNmzn+3JP82gV+2/kbnH++ec/74gv+eOdFshLjuarTFXDvvWSuj2GC36ecc47pSOjpCe2C2oF3KmNfnYYnuUx8JNV1X2ANysrLYn/q/pLvXWysuS0VzrZuhfR0GDjQBQMUEZF6S+Gs0Li+48jIy+CVpa9w6/9updOSP/HoZR4kbF0JixbBW2/Bp59C0RQWETkr3dLzFvqF9eOpeU+RkZtR0rExJqbC84OCICysepWzfEc+z8x7hu1Htlf9SU5Q1Ea/Y9OO5kBUFADNr/4znZt1ZvbO2eb4c89B3760feg5jrwGf+k/Dr78kpeH/Mb+JB/ef98svQNo4t2EAK8A9oft5+6A75m+rSPx8U4bcp2x66hpClNcOVu/3tz27Fl8zooV5laVMxERORMKZ4XOCz+PyJBIJi2dxM/bf+bxwY8T90AcfVsPgKFDYcIE8PJy9TBFxMXcLDfeHv02B9IO8PS8p6F/f1M2mjWr0sdUt2Pj68tfZ9LSSXy58cuqP8kJdqTsINw/HD9PP3Ng3jyTIrt0YXSH0Szcu5CsvCyTvKKieOrudrx5SwTW00+z8+nPeXXVUG67rWwnQsuyaBfUjj3H9jC+/zoctsXHHzttyHVGUbAtrpytXGk6+LZtW3zOihWmZ5T2NxMRkTOhcFbIsizev+x9Jl44kT0T9vDKxa/Q3FfdGEWkvPNbn8/9/e/n3dXvsvTYZhg5En74odJ1Z927m8JaVTo2xiTFMHHhRAC2HXHe3MgybfQdDpg/3+zdaFmM7jiarPwslvxhOjXGu6XzSqu95N57D7z4Ig9vHoOnp8Wrr5Z/3oigCPYe20vEwBZcziw+/dQmN9dpw64TiqeENisVzgYOLDOzYuVKUzXTZAsRETkTCmelDG07lOeGPUeIX4irhyIiddzLF79M26C2jJ0xltxrr4b9+2HNmgrPLerYuHfvmb1GgaOAsTPG4ufpx9C2Q9mW5LxwFpccV1L52bwZjhyBESMAuLDdhXi5e/Ha8td4ZekrPP774wBc1eUqfv0Vfv0VJk6Eli3LP29EUAR7ju3BjozkPt7j0CGrwe1JHZccRwu/FgR4BUBystmEvNT8xZQUswxN681ERORMKZyJiFRBY8/GTL5yMnHJcUwK2mKm//3wQ4XnVrVj47ur32VF/AreGf0Ow9oOY/fR3WaqYTUdzTrKkcwjJWum5hW2vS8MZ76NfLmu23VE7Y7iqXlP8W30t/QL60e7xl156CHo1g0efLDi524X1I7MvEyOdmzFSObSsUUq779f7SHXKWWqjkU7TZdKYkWHtN5MRETOlMKZiEgVjWg/gnHnjuPF6PdIGzqw0qmN3bub2zPp2JiYlsjT857mis5XcEvPW+ge3B2H7She71QdO1JO6NQYFWU6DYaHF5/zzXXfkPVMVvHHqrtX8cYbFrt2wTvvlDQBOVFEkwgAdga74ebZiP/rvohly2DTpmoPu87YkbKDzk0Lg+3KleDmVman6RUrzKH+/V00QBERqbcUzkREquHvQ/+Ow3awdlBb2LOnpHNfKUFBJvecSeVswd4FZOVn8cKwF7Asi+7BJuE5Y2pj0Zqpzs06Q24uLF5cXDUrYlkW3h7exR/x+92Kt3m8+OLKn7tdUDsA9qbHQ/fu3On2FT4+8Oab1R52nZCak8rB9IMllbMVK+Ccc8DPr/iclStN48bGjV00SBERqbcUzkREqqF1QGsCvAKY1cMTPDwqndrYq1eFua1SK+NX4tfIj54tTHv2zs064265OyWcxSXH4Wa50b5JezMHLzOzXDg70aOPmtt///vkz10UzvYc3QM9e9IkZjn33ANTppilWfVd0ebdnZp2MjtNr1pVZkqjw2EOaUqjiIhUhcKZiEg1WJZFZEgkq7N3wkUXVTq1ceBAM63x+PHTe96V8SvpH94fDzcPKCjA66FHmf2DD1c+8Rlcc40pRWVmVmnMO1J20CawDV4eXmZKo5sbDBtW6flRUebLeuYZaNPm5M8d4BVAU5+m7DlmwhkJCTx5z1G8vOD556s03DqlaFpp52adYft2SEsrE862bYPUVDUDERGRqlE4ExGppsjgSKIPR2Nffz3s3Gm6H55g0CCT2YqaRZxMVl4WGw5uYGB44W/4s2fD++/TNRn8E1NMKHjkEejQAcdbb2KfYUjbkbKjbDOQvn2hSZMKz921C265BTp1KqmenUpRO33OOQeAFoc2c//9MHXqma27q4uKpoR2aNrBzF+EMkls6VJzW3r/NxERkdOlcCYiUk2RIZGkZKVw+JLzzcZWM2aUO2fAAHPX8uWnfr4NBzeQ78hnYKvCX/o/+ghCQ/nki/vp+Ta/ctwAACAASURBVNd8crduNuvEunXD7eFHWDu8y2mP1bbtkjb66ekmLVYypfHwYRg1ykzV+/VX8PY+vdco2oianmZKJlu28NhjZlnWxImnPdQ6KS4ljtYBrfFt5GvCWZMm0Llz8f1Ll0JoqDafFhGRqlE4ExGppsiQSAA2OxJNtWjRonLnBARAZKTpH3EqK+NNRea8VufBvn0wcybcfTddW/akwC4w1ZshQzg041veGAR91sZDUtJpjTUpM4nUnFQTzlatgvx8GDq03Hnp6XD55ZCQYIJZqfxxShFBEew7tg9HaAto2hS2bKF5c3joIfj++/rduXFH8o6Tbj69bJmpmmnzaRERqQqFMxGRaioKZ9GHo+HCC015LDe33HmDBpk85HCc/PlWxq+kXVA7QhuHwqefmt/0x40r17Hxp+0/8WUv8HBAxndTTmusRY/t2ryrSRKWVdy9Ij8fliyBp54yMx3Xr4f//vfM109FNIkgpyCHgxmHTPWscJrnI49AYGD9rp7tSNlhgm1qqmm/WeqbEx9vNhofPNh14xMRkfpN4UxEpJqC/YIJ8QspCWdZWbB2bbnzBg0yDUFiYk7+fCviV5gpjbm5MHmyKWG1aUOXZl2wsIoD1vfbvic61CK2GeSdZjjbdNCUrXqH9jbhLDISgoJISTFFv6FD4fXXISzMNAG58soz+15AqXb6RevOoqPB4aBJExPQfv4Z1q078+d1teTMZFKyUsx6vTVrzCLCUuFs2TJzq/VmIiJSVQpnIiJOEBkSSXRSdMkUwYULy51T1F79ZFMb41PjiU+NZ1CrQTB9Ohw6BOPHA+DTyIf2Tdqz7cg2kjKSWLh3Ibf1up3/9oCAlRvMuaew8dBGQhuH0sKnuRnIBReQnw833WSaf3z5JRw5AgsWwLXXnul3wYgIMhtRF7XTJz3dTM/ETG1s2hT+8Y+qPbcrldm8+5dfzNYJAwYU3790qVlX17u3q0YoIiL1ncKZiIgTRAZHsi1pG45mTaFHjwrXnXXubILJycLZqnjTznFgq4Hw4YfQrp3pylGoe3B3tiVt4+ftP+OwHTw88GFm9vYlwRHGuOuOMHy42Yts796Kn39D4gZTNduyxbSBHzyYJ54w7fI//BDuuMNMPayOtkFtgcLKWammIGDW3j32GMyadXrr7+qSojb6XT3D4PPP4cYbzQ7jhZYuNYU0Dw9XjVBEROo7hTMRESeIDIkkPTedP47/YaY2LlsGeXllzrEs88v7yULJyviVeLl70TvD35Sv/vpXcHcvvr9HcA9ij8TybfS3dGzakbZevYiP+4iO7OCrFZ1ISoK//Q0iIkxR5+OPTQYDyC3IZVvSNnq36F08B++rgyN54w24/3646y7nfC98G/nSxLsJCWkJJqhCcTgD81rBwXW3evb2yreZGTez3PEdyTtws9yI+GmB+aY+/HDxfampZmmdpjSKiEh1KJyJiDhBuaYgGRmmo8YJBg0ya86OHq34eVYeWMm5Lc/Fc+48c+Cmm8rc3z24O3mOPBbsXcDV7W/mwgstEubcSreWP7Hd7kL03AR27YJXX4XsbDMjsmVLeO21Lnw0NZ68bA96h/Zmwy/x3OzzM2Meb86wYfDGG878bkB4QDgH0g6Av79JiqXCWePG8OSTplq3eLFzX9cZJi2dxCvLXil3fF3iOro37YLHex+Yrh/9+hXft3KlafSicCYiItWhcCYi4gQ9QkyFKPpwqXVnFUxtLFp3VtFm1HkFeaxNWGumNEZFmc2yTtgwq6hjI8CBnx5gyxYY99qvZF13KxHshR9+oH17M3Vw0yYTGm66CebPD2HCHe3hXylMvPlqzp3zMr/lX8Ljj1v8/DM0auSUb0OxMP8wUzkDUz07oQvKvfea0FjXqmcO20FyZjJrDqwhJz+nzPHl+5dzb2Ir2LPHLJ4rZelSU+A877zaHrGIiDQkCmciIk4Q4BVAm8A2JpyFhkKXLhWGswEDwM2t4qmNmw5tIjs/m0Gh/c2UxksuKXdO1+ZdAWiR+Be++yyECRPg5usaExsMaV3bm44e+fmAmUZ53nnw2WcwffpSrp30AR4DP6WJN7zMk/zx4le8/HL115hVJNy/sHIG0L07xMYWjwvAx8dMv1y0CDZscP7rV9XRrKMU2AXkFOSwPrGk8rn18FaO5xznujn7oW1buOaaMo9butQ0AvH3r+0Ri4hIQ1KtcGZZ1gTLsqIty9pqWdZDp36EiEjD1SO4hwlnYKY2Ll1aJpCA+eU9MtJshVZaXkEef5v7N3w8fLgoqbFZxHTxxeVew8/TjxEtbibz+/eJjIRXXimZUrn8xkFmKuUjj5R7nKenTUrLafQbM5Xlj/7Ek/yLwEsGlDvPWcL8wziYfpACR4EJZzk5puJUyl13ga8vvPtujQ3jjCVllmzmvfSPpcWfL9u/jN6JELpuOzzwQJmuH3l5pkKpKY0iIlJdVQ5nlmVFAuOAAUAv4ArLsjo6a2AiIvVNZEgkMUdiyHfkw7BhJmBt3FjuvNGjYd48mDu35NgTUU+waN8iPrnyE5otW2/KXhddVO6xOTngOeNbctL9+OYb8PaGEL8Qgn2D+aGvjwlm774L779f5nG2bbPx4MaSZiB+fmYPshoS7h+Ow3ZwKOOQCWdgNm0uJSjIdIecOtW0768qh+3gzp/v5JE55UPpmUrKKAlny/YvK/P5g5t9sH19YezYMo9Zv95sbadwJiIi1VWdylk3YJVt25m2becDi4DrnDMsEZH6JzIkktyCXHam7DSVM6hwauM//mGWYd1yC+zfD99u+ZY3V77JgwMe5LZzboPffzfNJpo2LfO4lBQz0/G33+Cdd8pmq+J91l591ewc/eCDMHt28f2Hcg5xPOc4fVr2MRW9QYNqtOd7eEA4AAdSD0BXMxWTbdvKnXfffSZwTp5c9dd6ecnLfLnpS2btmFX1JylUVDnrE9qHZfuXYds2YKpol+zzwBo2rEz7fDBbnrm7mzwuIiJSHdUJZ9HAEMuymlmW5QtcBrR2zrBEROqfMh0bw8KgUyezduwEfn7www8mlFx1XSZjf7qXIW2G8PrI1021beXKcuvNdu+G8883jUS++w7uuaf8a0cfjsZ2czOlqJ494eabi0tSO9N3AnCuX0fTOXHw4Br4DpQI8w8DME1B/P2hTZsKw1lkJAwfbvZYO2EG6GmZvXM2zy54Fi93r5I1btVQVDm7pus1HMk8QlxyHAdSD5AZv5dWCWklobuUH380h5s3r/bLi4jIWa7Kfza1bTvGsqx/AXOBDGAjUHDieZZl/RX4K0CLFi1YuHBhVV+yxqSnp9fJcUnDpWuuYcopyMENN6avmk7zw83p1K0bLX7/nWVRUdgVVKkefTSY55/vgZvHP5kwMYxlS5bRbPlyehYUsLF5c44VXiP79vny8MO9KSiweP31aFq0OM6Jl0+jo41Iz03nv3P+S6h3KL4PP0z/u+5i//33s3v8eLalbMMNN/zf+AYcDtYHB5Nag9dgck4yAAvWLSDwYCA9Q0PxXL2adRW85vDhzVmwIJKXX45myJDTn9+YkJXA+PXjifCLYGjzofxn33+YGTUTPw+/Ko979b7VAISlmXD52e+f4ePuw9B95v51jRuTVupr2LvXl+3bBzBqVBwLFyZU+XUbKr3XSW3TNSe1zenXnG3bTvkAJgH/d7Jz+vbta9dFCxYscPUQ5Cyja67h6vVhL3vk1yPNP77/3rbBtpcurfT81iO/t8G277/ftvPzbdt+4AHb9vW17exs27Zte+dO227Z0rZbtLDtmJjKX3fZH8tsJmL/GvtrycHbb7dtHx/bTkiwB7872B78cifb9ve37SuucMJXenL5Bfm2+/Pu9jPznjEHHnnEtr29C7/IsvLybLtNG9sePvzMXmPI50PsoFeC7J3JO+2pm6faTMTeenhrtcb9wKwH7ICXA2yHw2E3+1cze8zPY+wHZz1of3ieh+3w87Pt3Nwy5z//vG1blm0nJFTrZRssvddJbdM1J7WtKtccsNauJC9Vt1tjSOFtG8x6s6nVDYsiIvVZ/7D+rE1Ya/5oNXy4aewxb16F59q2Tdqwezjn2rm89x5cey1kzFlq9knz8iI+HkaMMNMfo6JKlm5VpGj/s+JukQDPPQe5ufDyy+xM38nEBbbpXPH66878kivk7uZOaOPQsu30s7Nh375y53p4mLVnCxZUvP9bRTJyM1j6x1IeHPAgHZp2KLvGrRqSMpMI9g3GsiwGtxnMsv3LWLZ/GaPiPbEGDy63IdyPP5rppi1bVutlRUREgOrvc/ajZVnbgF+A+2zbPuaEMYmI1Fv9w/uTkpXC7qO7oVkz6NOn0nC2P3U/x3JTGP/ULt5/H2bOtOkX9w03JLzNDTeY7n9Hj5qujpGRJ3/dIO8gWgW0YmtSqY6IHTrAXXdhf/wxfaIPcVHULrP7c5cuTvyKKxceEF6yEXVRx8YK1p2BGVbz5vD3v5/ec288uBEbm35h/cxr+ReGs2quO0vKSCLYLxiAwa0HE5ccxx+71hNxILPcerOdO2HzZrjhhmq9pIiISLFqhTPbtofYtt3dtu1etm1X/NuHiMhZZEC42TtsTcIac2DECLPjdEZGuXM3HjRt9nuH9ub//g+mj/0FPzLYntmG7dshJARmzYK+fU/vtSNDIlkRv4Lj2cdLDv797zhsBzO+hYLGfqaaVkvC/MNKKlndupnbSsKZvz889ZSpEFbQQ6Wcog2iz215bvFrgfMqZ2DCGcAFe03HxhPD2Y8/mtvr1KdYREScpLqVMxERKaVHcA+8PbxZfcA0luDii80uxUuWlDt348GNWFic0+IccDi4Yv4jrB30INE7vImOhtWrz6yp4tg+Y9lzdA/9Pu3HxoMbKXAU8MKe//BBn3waOcDx97+bal4tCfcvVTkLCjIdLCsJZ2CqZ+Hh8MwzUNjBvlLrEtfRwq+FCWUOBz533s3s/zbi8OE9J3/gKSRlJBHiFwJAv7B+eLl7MWwf2D4+0L9/mXN//NEcatOmWi8pIiJSTOFMRMSJGrk3ok9on5LK2QUXgKdnhVMbNx7cSOdmnfHz9INff4Vdu+Chh6r82jd0v4FFdy4iKy+LgZMHMvjzwTy38DmiH7iJLY9OwGvCw1V+7qoI8w/jaPZRsvKyzIHu3U8aznx84NlnTaFx1im2LFufuJ5zW56LZVnwwgswdSqjYvK4a+J0yMys0nht2+ZI5pHiypmXhxeDWg9i1AEfrPPPN/8dC+3bB2vWwPXXV+mlREREKqRwJiLiZP3D+rM+cT35jnzw9TUdI6Kiyp238eBGeof2Nv946y1o3brac+QGtxnMhns2MLTtUDYe3MgnV3zCR7d+S/IV15QJF7Wh3DqwonB2krLYXXdB+/Zm7ZnDUfE5WXlZbEvaRt+WfU2off55GDOG18afQ89tR+Dqq03jkzN0POc4eY684jVnAN8Me4/OB7LLTWn84Qdzq3AmIiLOpHAmIuJk/cP7k5mXSUxSjDkwYgRs3Fi8ITTAsexj7Dm2x4SzTZvMQqsHHjCtC6sp2C+YObfNIemxJMb1HWeqSy5QZiNqMOEsIwP276/0MY0amay1cWNJADrR5kObKbALGJITCrfdZhblvf8+Oy47jwk3+psq5a23nvF4izagLqqcAYRt2oVl2+XC2ZQpZkpjx45n/DIiIiKVUjgTEXGy/mFmbVKZpiBQptPF5kObAdMMhLfeMhW2u+922hgsy8Lfy99pz1cV5drbn6JjY5GOQ9fgFhLDTffF0miiD40nNWbxvsXF969LXEdECgx/8A0TZn/8EXx8CPcP571uaRQ8/RT89NNJQ2BFkjILw1mpyhmLFoGXFwwYUHwoOtqEx9tvP6OnFxEROSWFMxERJ+vUrBMBXgElTUH69zftCH//vficDYkbADjXLRymToU774QmTVww2ppTNK2xXDv9rVsreYTxc9yPcNE/4EgXRmV+jbeHN2+verv4/tR5s1gz2cIj5RhMnw5t25KZCUFEAHDw6sIwPH36GY23XOUsNxe+/97sO+ftXXze11+bTHjzzWf09CIiIqekcCYi4mRulhv9wvqVVM48PMw6qClT4ICpIm08tJEWfi0I+fArEwImTHDhiGtGgFcAvo18S9acNWtm9gc4ReVs3p55DLrkIH37QvS0G7i9+93MiJ3BodRE+PxzHnl2FpkBPlgrV8LgwaSmmsLWQ8PvgHdiufuVSL5s+QQ5P52iq8gJylXOpk411bdSTVoKCuCbb2D0aAgOruhZREREqk7hTESkBgwIG8DmQ5vJzs82B154wfxm//TTgGkGcm1uB3jzTRg7Fjp3duFoa4ZlWWXb6QP07Alr11b6mKNZR1mXsI6L24/gpZdMV8SAteN5YGk+nt17wtixLGsNX7w/Djp1oqDAVLBiY+Gu+w9DyFZWLvbnzsRXiJg/mddfyCQt7fTGW6Zy5nDAv/4FvXrBpZcWn7NwocnXmtIoIiI1QeFMRKQG9A/vT74jn00HN5kDERGmAvPVV+StWsG2g9E8OWUvNG0Kr77q0rHWpDD/sJLKGZiS0+bN8McfFZ6/YO8CbGwubn8xI0fCkD7pfPKCN/+c68NOrwx2ffASl9xm073rBQA89hj89hu89x689qoH3Hwdz07/iN/f3kY3YnjsOV86dID160891qTMJPwa+eHTyAd+/hm2b4cnn4RSDVW+/hoCAuDKK6v1bREREamQwpmISA0oagpSvO4MTNUsOJichx5gzJp82sYkwL//bQJaAxUecELlrCjV/PJLhefP2z0Pv0Z+DAgfgJWexqTDd3PQDuWaYcsZcHs2z4ZEU+AO57Y8l08+MYXHCRPgnnugiXcTvD28SUg/wMX3d2Ve6G2svOhpfHxMT5Y1a04+1qTMJDOl0bbh5ZehQwe44Ybi+zMzTe+RP/3J7MkmIiLibApnIiI1oFVAKzo17cQz85/hx20/moOBgfDCCzReuY53foOMIQNNK/gGLNw/nAOpB7CL9jbr0sVM4awsnO2Zx4XtLsTTrRGMH88Fid/z9zv28fvC3nivfZpvo78lyDuImVMiGD/eFOJef9081rIsWgW0Ij41Htzc4OqrOW/1uyyam0OTJnDxxWaD68okZSSZKY3z5pmpl48/XmZrg59/hvR0TWkUEZGao3AmIlIDLMsi6o4ougd354bvb+Ch2Q+x5dAWPjgnl10tvbEt8P7k8zJT5hqiMP8wcgpySMlKKTl41VUwfz6kppY5Nz41ntjkWEZEjIAvvjANOSZO5Pkv2nLDDZA960WIvYKg5e/w4IMWV10F//tf2a3hwv3DS6ZRXn01pKfTbtc8Fi0yvUhGjjRd9itSXDl79VVo2RL+8pfi+7Kzzf5rHTvCkCHO+u6IiIiUpXAmIlJD2gS2YfGYxUw4bwJvr3qbcz46h/t+n8Bt45ox9f3xuHft5uoh1rhy7fTBTG3My4O5c8ucO2/3PAAuy28P998PF10ETz+Nmxt8+SV075kN3/3M3hm3M26c2aT6xOmF4QHhJfuqXXQRNG4M06fTurXZsqxbN7juOnj0UTOE0pIykmhlBZngeOedZn+zQv/8J8TFwQcfmKKciIhITdCPGBGRGuTp7slbo98i6vYoPr/qc3Y/uJsVz8cz5p4PXT20WhHmHwZQtinI+eebdXYnTG2ct2ceLbyb0+Xxf5nUNWUKuLsDZo/uubN8ieiayqNPpfHxx2UrZkVa+bfiQFrhNEovL7jsMrPfmcNBWBgsWWJy3xtvwIUXwq5d5nG2bXM44zD99uaarprDhhU/5+bNpnHjX/4Cl1zi1G+PiIhIGRX8aBMREWcb0X6Eq4fgEuEBFVTOPDzg8sth5kzIzwcPD2zbJmp3FK/GtDL7l02ZYqYWln6ucNi97eQbdYcHhJNbkMuRzCNmiuKVV8K0aaZdY79+eHnBu++aqYl33w2RkfDMMzD+wXRysiFhUS9GczdNPh3OOI+S85o2Nb1bREREapIqZyIiUmNaNjYBq3iqYZErr4Tk5OIOHduPbMd3XyK3fLcNrrgCbrmlSq9XNI2yuFI3apRZ1zd7dpnzbrwRYmLMMJ59Fvr28oY3DjBx6d+J84pkzrxGjBhhAuGaNfDOO2YPbRERkZqkcCYiIjXGy8OLEL8Qfon7pXiTZwBGjcJu1IiYL15j/K/juWLKZUyeAZaXF3z0UZUbpRRV6orDYHAw9O1bLpyBCV7Tppl90vyb5ODedh5z3Ueyc8J7HDhginc9esBdd5kwJyIiUtM0rVFERGrUGyPfYOyMsfT5uA//veG/nNPiHN7Z+C4D29kM+88v/HsKeOCGVx7w2VsmNVVRq4BWgOn8WGz0aJg0CY4ehSblp0WOHg357Rfy5ks3ccl2YNjD+PjArbeaDxERkdqicCYiIjXq1nNupXtwd/70/Z8Y9uUw/D39OZp9lAfGDKHL3nDC/MNws9zMps9jxlTrtUIbh+JmuZVtQDJ6tGm3GBVldpCuQFJGEhfuBdvNDWvw4GqNQUREpKoUzkREpMb1admHdX9dx32z7iM1J5Vnhz5L//D+Tn8dDzcPWvi1KLvG7bzzICjITG2sLJxlJnHhPnD06Y17QIDTxyUiInI6FM5ERKRWBHoHMuW6KTX+Oq0CWhGfVmpao4eH6YE/ezbYdvF6tvjUeML9w7Esi6NHEznvALhdP6zGxyciIlIZNQQREZEGpcxG1EVGj4aEBIiOBuDXuF9p/WZrJi2ZBEDAxhi888EaPry2hysiIlJM4UxERBqUHsE9iDkSw9qEtSUHR40yt7Nncyz7GPf8eg8WFs8vep6th7fSesNuHBZwwQUuGbOIiAgonImISAPzt/P/RmjjUO6afhe5BbnmYHg49OwJs2fz6JxHOZR+iN9u/Y1A70DGTB9Dl62J7G7jb9amiYiIuIjCmYiINChB3kF8fMXHbDm8pXjaIgCjR+NYspjzn/+cLb9FMOrWf7DnbTfm37eG/rHp7OxZ9Rb+IiIizqBwJiIiDc4Vna/g1p638tKSl9h8aDM5+TmsH9qJNI8CLt/jQZcsPwgKwm/4SOZf0oEnLoYVtwx19bBFROQsp26NIiLSIL09+m1+3/07I74aQUZuBln5Wfg848O8O+YR2noQABbQPy2R+yYP4KXu2t9MRERcS+FMREQapGa+zfji6i94cfGLDAwfyPCI4QxpM4QmPk3KnNfSvyV/PPQHVmGLfREREVdROBMRkQbrsk6XcVmny055noKZiIjUBVpzJiIiIiIiUgconImIiIiIiNQBCmciIiIiIiJ1gMKZiIiIiIhIHaBwJiIiIiIiUgconImIiIiIiNQBCmciIiIiIiJ1gMKZiIiIiIhIHaBwJiIiIiIiUgconImIiIiIiNQBCmciIiIiIiJ1gMKZiIiIiIhIHaBwJiIiIiIiUgconImIiIiIiNQBCmciIiIiIiJ1gMKZiIiIiIhIHaBwJiIiIiIiUgconImIiIiIiNQBCmciIiIiIiJ1gMKZiIiIiIhIHaBwJiIiIiIiUgconImIiIiIiNQBCmciIiIiIiJ1gGXbdu29mGUlAftq7QVPX3PgiKsHIWcVXXNS23TNiSvoupPapmtOaltVrrm2tm0HV3RHrYazusqyrLW2bfdz9Tjk7KFrTmqbrjlxBV13Utt0zUltc/Y1p2mNIiIiIiIidYDCmYiIiIiISB2gcGZ84uoByFlH15zUNl1z4gq67qS26ZqT2ubUa05rzkREREREROoAVc5ERERERETqgLM6nFmWNdqyrFjLsnZalvWkq8cjDZdlWXsty9piWdZGy7LWFh5ralnW75Zl7Si8beLqcUr9ZVnW55ZlHbYsK7rUsQqvMct4p/C9b7NlWee6buRSX1VyzU20LOtA4XvdRsuyLit131OF11ysZVmjXDNqqc8sy2ptWdYCy7K2WZa11bKsCYXH9V4nNeIk11yNvdedteHMsix34H3gUqA78GfLsrq7dlTSwA23bbt3qXarTwLzbNvuBMwr/LdIVf0HGH3CscqusUuBToUffwU+rKUxSsPyH8pfcwBvFr7X9bZtexZA4c/Xm4EehY/5oPDnsMiZyAcetW27OzAQuK/w2tJ7ndSUyq45qKH3urM2nAEDgJ22be+2bTsX+A642sVjkrPL1cCXhZ9/CVzjwrFIPWfb9mIg5YTDlV1jVwNf2cZKIMiyrJa1M1JpKCq55ipzNfCdbds5tm3vAXZifg6LnDbbthNt215f+HkaEAOEo/c6qSEnueYqU+33urM5nIUD+0v9O56Tf7NFqsMG5lqWtc6yrL8WHmth23Zi4ecHgRauGZo0YJVdY3r/k5p0f+EUss9LTdfWNSdOZVlWO6APsAq910ktOOGagxp6rzubw5lIbbrAtu1zMVMs7rMsa2jpO23TNlWtU6XG6BqTWvIh0AHoDSQC/3btcKQhsiyrMfAj8JBt26ml79N7ndSECq65GnuvO5vD2QGgdal/tyo8JuJ0tm0fKLw9DPyEKXEfKppeUXh72HUjlAaqsmtM739SI2zbPmTbdoFt2w7gU0qm8+iaE6ewLKsR5pfkb2zb/l/hYb3XSY2p6Jqryfe6szmcrQE6WZYVYVmWJ2bx3gwXj0kaIMuy/CzL8i/6HBgJRGOut78UnvYXYLprRigNWGXX2AzgjsJOZgOB46WmBIlU2Qnrea7FvNeBueZutizLy7KsCEyDhtW1PT6p3yzLsoDPgBjbtt8odZfe66RGVHbN1eR7nUf1hlx/2badb1nW/cAcwB343LbtrS4eljRMLYCfzP/feABTbduebVnWGmCaZVljgX3AjS4co9RzlmV9CwwDmluWFQ88B7xCxdfYLOAyzELlTGBMrQ9Y6r1KrrlhlmX1xkwr2wvcA2Db9lbLsqYB2zDdz+6zbbvAFeOWem0wcDuwxbKsjYXHnkbvdVJzKrvm/lxT73WWmZorIiIiIiIirnQ26h+SqwAAAFRJREFUT2sUERERERGpMxTORERERERE6gCFMxERERERkTpA4UxERERERKQOUDgTERERERGpAxTORERERERE6gCFMxERERERkTpA4UxERERERKQO+H9RRiPznuIbUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "fig = plt.plot(df1.close, 'g',label='close')\n",
    "plt.plot(df1.ma1, 'r', label='ma1')\n",
    "plt.plot(df.ma2, 'b',label='ma2')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b9ed8f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>ma1</th>\n",
       "      <th>ma2</th>\n",
       "      <th>trend</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20181228</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.46</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.28</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.0776</td>\n",
       "      <td>576604.00</td>\n",
       "      <td>541571.004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20181227</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.49</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.30</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.2151</td>\n",
       "      <td>624593.27</td>\n",
       "      <td>586343.755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20181226</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.42</td>\n",
       "      <td>9.27</td>\n",
       "      <td>9.30</td>\n",
       "      <td>9.34</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.4283</td>\n",
       "      <td>421140.60</td>\n",
       "      <td>393215.140</td>\n",
       "      <td>9.320000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20181225</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.21</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.42</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.8493</td>\n",
       "      <td>586615.45</td>\n",
       "      <td>545235.607</td>\n",
       "      <td>9.306667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20181224</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>9.40</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.42</td>\n",
       "      <td>9.45</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.3175</td>\n",
       "      <td>509117.67</td>\n",
       "      <td>477186.904</td>\n",
       "      <td>9.353333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180108</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>13.25</td>\n",
       "      <td>13.29</td>\n",
       "      <td>12.86</td>\n",
       "      <td>12.96</td>\n",
       "      <td>13.30</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-2.5600</td>\n",
       "      <td>2158620.81</td>\n",
       "      <td>2806099.169</td>\n",
       "      <td>13.170000</td>\n",
       "      <td>13.551429</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180105</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>13.21</td>\n",
       "      <td>13.35</td>\n",
       "      <td>13.15</td>\n",
       "      <td>13.30</td>\n",
       "      <td>13.25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3800</td>\n",
       "      <td>1210312.72</td>\n",
       "      <td>1603289.517</td>\n",
       "      <td>13.113333</td>\n",
       "      <td>13.422857</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180104</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>13.32</td>\n",
       "      <td>13.37</td>\n",
       "      <td>13.13</td>\n",
       "      <td>13.25</td>\n",
       "      <td>13.33</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.6000</td>\n",
       "      <td>1854509.48</td>\n",
       "      <td>2454543.516</td>\n",
       "      <td>13.170000</td>\n",
       "      <td>13.287143</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180103</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>13.73</td>\n",
       "      <td>13.86</td>\n",
       "      <td>13.20</td>\n",
       "      <td>13.33</td>\n",
       "      <td>13.70</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-2.7000</td>\n",
       "      <td>2962498.38</td>\n",
       "      <td>4006220.766</td>\n",
       "      <td>13.293333</td>\n",
       "      <td>13.255714</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180102</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>13.35</td>\n",
       "      <td>13.93</td>\n",
       "      <td>13.32</td>\n",
       "      <td>13.70</td>\n",
       "      <td>13.30</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3.0100</td>\n",
       "      <td>2081592.55</td>\n",
       "      <td>2856543.822</td>\n",
       "      <td>13.426667</td>\n",
       "      <td>13.298571</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
       "20181228    000001.SZ   9.31   9.46   9.31   9.38       9.28    0.10   1.0776   \n",
       "20181227    000001.SZ   9.45   9.49   9.28   9.28       9.30   -0.02  -0.2151   \n",
       "20181226    000001.SZ   9.35   9.42   9.27   9.30       9.34   -0.04  -0.4283   \n",
       "20181225    000001.SZ   9.29   9.43   9.21   9.34       9.42   -0.08  -0.8493   \n",
       "20181224    000001.SZ   9.40   9.45   9.31   9.42       9.45   -0.03  -0.3175   \n",
       "...               ...    ...    ...    ...    ...        ...     ...      ...   \n",
       "20180108    000001.SZ  13.25  13.29  12.86  12.96      13.30   -0.34  -2.5600   \n",
       "20180105    000001.SZ  13.21  13.35  13.15  13.30      13.25    0.05   0.3800   \n",
       "20180104    000001.SZ  13.32  13.37  13.13  13.25      13.33   -0.08  -0.6000   \n",
       "20180103    000001.SZ  13.73  13.86  13.20  13.33      13.70   -0.37  -2.7000   \n",
       "20180102    000001.SZ  13.35  13.93  13.32  13.70      13.30    0.40   3.0100   \n",
       "\n",
       "                   vol       amount        ma1        ma2  trend  pos  \n",
       "trade_date                                                             \n",
       "20181228     576604.00   541571.004        NaN        NaN      0  NaN  \n",
       "20181227     624593.27   586343.755        NaN        NaN      0  0.0  \n",
       "20181226     421140.60   393215.140   9.320000        NaN      0  0.0  \n",
       "20181225     586615.45   545235.607   9.306667        NaN      0  0.0  \n",
       "20181224     509117.67   477186.904   9.353333        NaN      0  0.0  \n",
       "...                ...          ...        ...        ...    ...  ...  \n",
       "20180108    2158620.81  2806099.169  13.170000  13.551429      0  0.0  \n",
       "20180105    1210312.72  1603289.517  13.113333  13.422857      0  0.0  \n",
       "20180104    1854509.48  2454543.516  13.170000  13.287143      0  0.0  \n",
       "20180103    2962498.38  4006220.766  13.293333  13.255714      0  0.0  \n",
       "20180102    2081592.55  2856543.822  13.426667  13.298571      0  0.0  \n",
       "\n",
       "[243 rows x 14 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.set_index('trade_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "04792659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2018-12-28\n",
       "1   2018-12-27\n",
       "2   2018-12-26\n",
       "3   2018-12-25\n",
       "4   2018-12-24\n",
       "Name: trade_date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "164734b1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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